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  2. Large-scale multi-center CT and MRI segmentation of pancreas with deep learning (PanSegNet)

    Authors: Zheyuan Zhang et al.

    MEDICAL IMAGE ANALYSIS

    [Paper]
    [Code]
    [Data]
  1. ControlPolypNet- Towards Controlled Colon Polyp Synthesis for Improved Polyp Segmentation

  2. An objective validation of polyp and instrument segmentation methods in colonoscopy through Medico 2020 polyp segmentation and MedAI 2021 transparency challenges

    Authors: Debesh Jha, Vanshali Sharma, Debapriya Banik, Debayan Bhattacharya, Kaushiki Roy, Steven A. Hicks, Nikhil Kumar Tomar, Vajira Thambawita, Adrian Krenzer, Ge-Peng Ji, Sahadev Poudel, George Batchkala, Saruar Alam, Awadelrahman M. A. Ahmed, Quoc-Huy Trinh, Zeshan Khan, Tien-Phat Nguyen, Shruti Shrestha, Sabari Nathan, Jeonghwan Gwak, Ritika K. Jha, Zheyuan Zhang, Alexander Schlaefer, Debotosh Bhattacharjee, M.K. Bhuyan, Pradip K. Das, Deng-Ping Fan, Sravanthi Parsa, Sharib Ali, Michael A. Riegler, Pål Halvorsen, Thomas De Lange, Ulas Bagci

    Medical Image Analysis (International competition paper)

    [Paper]

  2. Healthy-to-Patients Deep learning for Automated Left Atrial Segmentation and Function analysis from Short-Axis Cine CMR 

    Authors: M. Elbayumi et al.

    SCMR 2025

    [Paper]

  2. HCC Detection in the Wild: AI models for automated MRI HCC detection.

    Authors: Matthew Antalek, Robert Lewandowski, and Ulas Bagci

    SIR 2025 Annual Scientific Meeting

    2. Mortality Prediction of Pulmonary Embolism Patients with Deep Learning and XGBoost

      Authors: Vedat Cicek et al.

      IEEE ICECCME 2024

      [Paper]

    2. Machine learning for prognostic prediction in coronary artery disease with SPECT data: a systematic review and meta-analysis

      Authors: Vedat Cicek, et al.

      EJNMMI Research

      [Paper]

    2. Evidential Federated Learning for Skin Lesion Image Classification

      Authors: Rutger Hendrix et al

      ICPR 2024

      [Paper]

    2. AI-powered contrast-free cardiovascular magnetic resonance imaging for myocardial infarction

      Authors: Vedat Cicek and Ulas Bagci

      FRONTIERS CARDIOVASCULAR MEDICINE

      [Paper]
    1. Optimizing Synthetic Data for Enhanced Pancreatic Tumor Segmentation

    2. Optimizing Synthetic Data for Enhanced Pancreatic Tumor Segmentation

      Authors: Linkai Peng, Zheyuan Zhang, Gorkem Durak, Frank H. Miller, Alpay Medetalibeyoglu, Michael B. Wallace, Ulas Bagci

      MICCAI AIPAD 2024

      [Paper]

    2. Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation

      Authors: AKoushik Biswas, Ridal Pal, Shaswat Patel, Debesh Jha, Meghana Karri, Amit Reza, Gorkem Durak, Alpay Medetalibeyoglu, Matthew Antalek, Yury Velichko, Daniela Ladner, Amir Borhani, Ulas Bagci

      MICCAI MLMI 2024

      [Paper]
    1. Classification of Endoscopy and Video Capsule Images using Hybrid Model

    2. Classification of Endoscopy and Video Capsule Images using Hybrid Model

      Authors: Aliza Subedi, Smriti Regmi, Nisha Regmi, Bhumi Bhusal, Ulas Bagci, Debesh Jha

      MICCAI CaPTion 2024

      [Paper]

    2. Practical and Ethical Considerations for Generative AI in Medical Imaging

      Authors: Debesh Jha, Ashish Rauniyar, Desta Haileselassie Hagos, Vanshali Sharma,Nikhil Kumar Tomar, Zheyuan Zhang, Ilkin Isler, Gorkem Durak, Michael Wallace, Cemal Yazici, Tyler Berzin, Koushik Biswas, Ulas Bagci

      MICCAI EPIMI 2024

      [Paper]
    1. AI-ADC: Channel and Spatial Attention-Based Contrastive Learning to Generate ADC Maps from T2W MRI for Prostate Cancer Detection

    2. AI-ADC: Channel and Spatial Attention-Based Contrastive Learning to Generate ADC Maps from T2W MRI for Prostate Cancer Detection

      Authors: Ozyoruk et al.

      Journal of Personalized Medicine.

      [Paper]

    2. Advancing Pediatric Brain Tumor Diagnosis: A Deep Learning Approach to Multiclass Segmentation in CBTN data: Feasibility Study

      Authors: Max Bengtsson, et al

      CBTN (Children Brain Tumor Network) Summit 2024

      [Paper]

    2. The association of volumetric changes and disease severity with endocrine dysfunction following acute pancreatitis

      Authors: Elif Keles, Gorkem Durak,Jasson Barrios, Ziliang Hong, Paya Sarraf,Matthew Antalek, Zheuyan Zhang, Brian Boulay, Ashley Vareedayah, Brian Layden,Temel Tirkes, Ulas Bagci,Cemal Yazici,

      APA/JPS/CAP/IAP 2024 Annual Meeting

      [Paper]

    2. Patients with acute pancreatitis have significant decline in pancreas volume over time 

      Authors: Jasson Barrios, Elif Keles, Gorkem Durak, Ziliang Hong, Paya Sarraf,Matthew Antalek, Zheuyan Zhang, Brian Boulay, Ashley Vareedayah, Brian Layden,Temel Tirkes,Ulas Bagci,Cemal Yazici

      APA/JPS/CAP/IAP 2024 Annual Meeting

      [Paper]

    2. Deep learning based automated pancreas segmentation and volumetry measurement in patients with acute pancreatitis

      Authors: Elif Keles, Gorkem Durak, Ziliang Hong, Jasson Barrios, Paya Sarraf,Matthew Antalek, Zheuyan Zhang, Brian Boulay, Ashley Vareedayah, Brian Layden,Temel Tirkes, Cemal Yazici, Ulas Bagci

      APA/JPS/CAP/IAP 2024 Annual Meeting

      [Paper]

    2. EyeSee: Integrating Deep Learning for ImageAnalysis

      Authors: Bin Wang and Ulas Bagci

      Medical Image Perception Society (MIPS) XX 2024.

      [Paper]

    2. Early stage lung cancer detection from speech sounds in natural environments.

      Authors: Haydar Ankishan, et al.

      Biomedical Signal Processing and Control

      [Paper]

    2. Deformable Capsules: A Novel Capsule Network Architecture for Object Detection.”

      Authors: Naji Khosravan, Rodney LaLonde, Ulas Bagci

      Advanced Intelligent Systems (Willey)

      [Paper]
      [Code]

    2. UTILIZING CHEST RADIOGRAPHS (CXRS) FOR KIDNEY FAILURE PROGNOSTICATION AND EARLY RISK STRATIFICATION WITH PROSPECTIVE AND EXTERNAL VALIDATION

      Authors: Theo Dapamede et al.

      RSNA 2024

    2. MRI INTENSITY STANDARDIZATION VIA PROTOTYPE LEARNING FOR IMPROVED ONCOLOGICAL ANALYSIS OF HETEROGENOUS DATASETS

      Authors: Meghana Karri, et al.

      RSNA 2024

    2. LIMITED UTILITY OF RECIST 1.1 IN PREDICTING PATHOLOGIC RESPONSE TO NEOADJUVANT RADIOTHERAPY IN MYXOID LIPOSARCOMA

      Authors: Yuri Velichko et al.

      RSNA 2024

    2. INCREASED TUMOR SIZE ON MRI PREDICTS PATHOLOGICAL RESPONSE TO NEOADJUVANT RADIOTHERAPY IN UNDIFFERENTIATED PLEOMORPHIC SARCOMA

      Authors: Yuri Velichko et al.

      RSNA 2024

    2. EYESEE: REAL RADIOLOGY ROOM EXPERIENCE WITH EYE TRACKING AND DEEP LEARNING

      Authors: Bin Wang, et al.

      RSNA 2024

    2. DETECTION OF PERI-PANCREATIC EDEMA USING DEEP LEARNING AND RADIOMICS

      Authors: ZIliang Hong, et al.

      RSNA 2024

    2. ENHANCED PREDICTION OF LUNG CANCER RISK FROM IMAGING BIOMARKERS IN CHEST RADIOGRAPHS: AN EXTERNAL VALIDATION STUDY

      Authors: Theo Dapamede et al.

      RSNA 2024

    2. IMAGING ASSESSMENT OF RESPONSE IN SOFT TISSUE SARCOMA AFTER NEOADJUVANT RADIOTHERAPY

      Authors: Yuri Velichko et al.

      RSNA 2024

    2. PGDS: Pose-Guidance Deep Supervision for Mitigating Clothes-Changing in Person Re-Identification

      Authors: Quoc-Huy Trinh, Nhat-Tan Bui, Dinh-Hieu Hoang, Phuoc-Thao Vo Thi, Hai-Dang Nguyen, Debesh Jha, Ulas Bagci, Ngan Le, Minh-Triet Tran

      IEEE AVSS 2024

      [Paper]

    2. SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation

      Authors: Quoc-Huy Trinh, Hai-Dang Nguyen, Bao-Tram Nguyen Ngoc, Debesh Jha, Ulas Bagci, Minh-Triet Tran

      BMVC 2024

      [Paper]

    2. AI-Powered Road Network Prediction with Multi-Modal Data

      Authors: Necip Gengeç et al.

      Earth Science Informatics. 2023. link to be published

      [Paper]
      [Code]
    1. ControlPolypNet- Towards Controlled Colon Polyp Synthesis for Improved Polyp Segmentation

    2. ControlPolypNet: Towards Controlled Colon Polyp Synthesis for Improved Polyp Segmentation

      Authors: Vanshali Sharma, Abhishek Kumar, Debesh Jha, M.K. Bhuyan, Pradip K. Das, Ulas Bagci

      CVPR Workshop

      [Paper]
    1. PP-SAM- Perturbed Prompts for Robust Adaption of Segment Anything Model (SAM) for Polyp Segmentation

    2. PP-SAM: Perturbed Prompts for Robust Adaption of Segment Anything Model (SAM) for Polyp Segmentation

      Authors: Md Mostafijur Rahman, Mustafa Munir, Debesh Jha, Ulas Bagci, Radu Marculescu

      CVPR Workshop

      [Paper]
    1. Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans

    2. Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans

      Authors: Koushik Biswas, Debesh Jha, Nikhil Kumar Tomar, Gorkem Durak, Alpay Medetalibeyoglu, Matthew Antalek, Yury Velichko, Daniela Ladner, Amir Bohrani, Ulas Bagci

      MICCAI 2024

      [Paper]

    2. The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review

      Authors: Elif Keles & Ulas Bagci

      Nature Digital Medicine

      [Paper]

    2. Multichannel Orthogonal Transform-Based Perceptron Layers for Efficient ResNets

      Authors: H. Pan, E. Hamdan, X. Zhu, S. Atici and A. E. Cetin

      IEEE Transactions on Neural Networks and Learning Systems

      [Paper]
      [Code]
    1. TransRUPNet for Improved Out-of-Distribution Generalization in Polyp Segmentation

    2. TransRUPNet for Improved Out-of-Distribution Generalization in Polyp Segmentation

      Authors: D. Jha, N. K. Tomar, D. Bhattacharya, K. Biswas, U. Bagci

      Proceedings of the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EBMC), 2024

      [Paper]
      [Code]

    2. PAM-UNet: Shifting Attention on Region of Interest in Medical Images

      Authors: A. Das, D. Jha, V. Gorade, K. Biswas, H. Pan, Z. Zhang, D. P. Ladner, Y. Velichko, A. Borhani, and U. Bagci

      Proceedings of the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EBMC), 2024

      [Paper]

    2. Detection of Peri-Pancreatic Edema using Deep Learning and Radiomics Techniques

      Authors: Z. Hong, D. Jha, K. Biswas, Z. Zhang, Y. Velichko, C. Yazici, T. Tirkes, A. Borhani, B. Turkbey, A. Medetalibeyoglu, G. Durak, U. Bagci

      Proceedings of the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EBMC), 2024.

      [Paper]
      [Code]
      [Data]
    1. PP-SAM: Perturbed Prompts for Robust Adaption of Segment Anything Model for Polyp Segmentation

    2. PP-SAM: Perturbed Prompts for Robust Adaption of Segment Anything Model for Polyp Segmentation

      Authors: Md M. Rahman, M. Munir, D. Jha, U. Bagci, R. Marculescu

      Proceedings of the CVPR Workshop on Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis (CVPR DEF-AI-MIA), 2024.

      [Paper]
      [Code]

    2. ControlPolypNet: Towards Controlled Colon Polyp Synthesis for Improved Polyp Segmentation

      Authors: V. Sharma, A. Kumar, D. Jha, M.K. Bhuyan, P. Das, U. Bagci.

      Proceedings of the Data Curation and Augmentation in Enhancing Medical Imaging Applications Workshop (DCAMI 202), CVPR 2024

      [Paper]
      [Code]

    2. Predicting Short Term Mortality In Patients With Acute Pulmonary Embolism With Deep Learning

      Authors: V Cicek, et al.

      ICNC-CT 2024

    1. A review of prognostic prediction of coronary artery disease patients with myocardial perfusion scintigraphy and artificial intelligence

    2. A review of prognostic prediction of coronary artery disease patients with myocardial perfusion scintigraphy and artificial intelligence

      Authors: V. Cicek, et al.

      ICNC-CT 2024

      [Paper]

    2. Healthy-to-Patients Domain-Adaptive Deep Learning for Time-Resolved Segmentation of Left Atrium in Short-Axis Cine MRI Images

      Authors: M. Elbayumi, et al.

      ISMRM 2024

    1. ENHANCING COLONOSCOPY OUTCOMES WITH DAPODET-BASED AI FOR REAL-TIME SESSILE SERRERATED POLYP DETECTION.

    2. ENHANCING COLONOSCOPY OUTCOMES WITH DAPODET-BASED AI FOR REAL-TIME SESSILE SERRERATED POLYP DETECTION.

      Authors: Das, Abhijit, et al.

      DDW 2024

    2. THE BOSTON ERCP DATASET: A VIDEO DATASET FOR ADVANCED ENDOSCOPY

      Authors: Mark E. Geissler, et al.

      DDW 2024

    1. ENHANCING LIVER SEGMENTATION OUTCOMES WITH MSFORMER-BASED ARTIFICIAL INTELLIGENCE SYSTEM

    2. ENHANCING LIVER SEGMENTATION OUTCOMES WITH MSFORMER-BASED ARTIFICIAL INTELLIGENCE SYSTEM

      Authors: Jha, Debesh, et al.

      DDW 2024

    1. Evaluation of pan-Immuno-Inflammation value for In-hospital mortality in acute pulmonary embolism patients

    2. Evaluation of pan-Immuno-Inflammation value for In-hospital mortality in acute pulmonary embolism patients

      Authors: Çiçek, Vedat, et al.

      Revista de Investigacion Clinica; Organo del Hospital de Enfermedades de la Nutricion

      [Paper]

    2. A Probabilistic Hadamard U-Net for MRI Bias Field Correction

      Authors: Zhu, Xin, et al.

      MLMI-MICCAI 2024

      [Paper]
    1. Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor Segmentation

    2. Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor Segmentation

      Authors: Gorade, Vandan, et al.

      IEEE ISBI 2024

      [Paper]
      [Code]
    1. FuseNet- Self-Supervised Dual-Path Network for Medical Image Segmentation

    2. FuseNet: Self-Supervised Dual-Path Network for Medical Image Segmentation

      Authors: Kazerouni, Amirhossein, et al.

      IEEE ISBI 2024

      [Paper]
      [Code]

    2. Explainable Transformer Prototypes for Medical Diagnoses.

      Authors: Demir, Ugur, et al.

      IEEE ISBI 2024

      [Paper]
      [Code]

    2.  Leveraging Unlabeled Data for 3D Medical Image Segmentation through Self-Supervised Contrastive Learning

      Authors: Karimijafarbigloo, Sanaz, et al.

      IEEE ISBI 2024

      [Paper]
      [Code]

    2. Prediction of MRI-Induced Power Absorption in Patients with DBS Leads

      Authors: Yalcin Tur, et al.

      IEEE EMBS 2024

      [Paper]

    2. Domain Generalization with Fourier Transform and Soft Thresholding.

      Authors: Pan, Hongyi, et al.

      IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2024

      [Paper]
      [Code]
    1. Federated Learning for Medical Applications- A Taxonomy, Current Trends, Challenges, and Future Research Directions

    2. Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions.

      Authors: Rauniyar, Ashish, et al.

      IEEE Internet of Things Journal

      [Paper]

    2. Artificial Intelligence and Infectious Disease Imaging.

      Authors: Chu, Winston T., et al.

      The Journal of Infectious Diseases

      [Paper]

    2. Methods of artificial intelligence-assisted infrastructure assessment using mixed reality systems.

      Authors: Karaaslan, Enes, et al.

      US Patent 2024

      [Paper]

    2. TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing.

      Authors: Jha, Debesh, et al.

      Proceedings of Machine Learning Research 2024

      [Paper]
      [Code]

    2. Deep Learning-Based Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study.

      Authors: Belue, Mason J., et al.

      Academic Radiology

      [Paper]
      [Code]

    2. Harmonized Spatial and Spectral Learning for Robust and Generalized Medical Image Segmentation

      Authors: Gorade, Vandan, et al.

      ICPR 2024

      [Paper]

    2. CT Liver Segmentation via PVT-based Encoding and Refined Decoding

      Authors: Jha, Debesh, et al.

      IEEE ISBI 2024 (Oral)

      [Paper]
      [Code]

    2. AI powered road network prediction with fused low-resolution satellite imagery and GPS trajectory

      Authors: Gengec, Necip Enes, et al.

      Earth Science Informatics

      [Paper]
      [Code]

    2.  GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-Ray Classification.

      Authors: Wang, Bin, et al.

      WACV 2024

      [Paper]
      [Code]

    2. INCODE: Implicit Neural Conditioning With Prior Knowledge Embeddings

      Authors: Kazerouni, Amirhossein, et al.

      WACV 2024

      [Paper]
      [Code]

    2. SynergyNet: Bridging the Gap Between Discrete and Continuous Representations for Precise Medical Image Segmentation.

      Authors: Gorade, Vandan, et al.

      WACV 2024

      [Code]

    2. Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation.

      Authors: Azad, Reza, et al.

      WACV 2024

      [Paper]
      [Code]
    1. Domain Generalization With Correlated Style Uncertainty

    2. Domain Generalization with Correlated Style Uncertainty.

      Authors: Zhang, Zheyuan, et al.

      WACV 2024

      [Paper]
      [Code]

      2. An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation

        Authors: Srivastava, Abhishek, et al.

        2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

        [Paper]

      2. RUPNet: residual upsampling network for real-time polyp segmentation

        Authors: Tomar, Nikhil Kumar, Ulas Bagci, and Debesh Jha.

        Medical Imaging 2023: Computer-Aided Diagnosis. Vol. 12465. SPIE, 2023.

        [Paper]

      2. DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation

        Authors: Tomar, NK., Jha, D., Bagci, U.

        SPIE Medical Imaging 2023

        [Paper][Code]

      2. A Fully Automatic AI System for Pancreas Segmentation from Multicenter MRI Scans.

        Authors: Zheyuan Zhang, et al.

        DDW 2023

      2. AI Empowered Automatic Volume Delineation of Liver from CT Scans for Diagnostic Workflow

        Authors: Ugur Demir, Zheyuan Zhang, Bin Wang, et al.

        DDW 2023

      2. Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification

        Authors: Ilkin Isler, Debesh Jha, Curtis Lisle, et al.

        IEEE ICECCME 2023.

        [Paper]

      2. The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review

        Authors: Keles, Elif, and Ulas Bagci.

        npj Digital Medicine 220 (2023).

        [Paper]

      2. TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing

        Authors: Debesh Jha et al.

        MIDL 2023.

        [Paper][Code]

      2. Ensemble Learning with Residual Transformer for Brain Tumor Segmentation

        Authors: Yao, Lanhong, et al.

        ISBI, 2023.

        [Paper]

      2. Relational reasoning network for anatomical landmarking

        Authors: Torosdagli, Neslisah, et al.

        Journal of Medical Imaging, 024002 (2023)

        [Paper]

      2. AI in Clinical Medicine: A Practical Guide for Healthcare Professionals

        Authors: Michael F. Byrne (Editor), Nasim Parsa (Co-Editor), Alexandra T. Greenhill (Co-Editor), Daljeet Chahal (Co-Editor), Omer Ahmad (Co-Editor), Ulas Bagci (Co-Editor).

        [Paper]

      2. A multi-centre polyp detection and segmentation dataset for generalisability assessment

        Authors: Ali, Sharib, et al.

        Scientific Data 10.1 (2023): 75.

        [Paper][Code]

      2. GazeSAM: What you see is what you segment

        Authors: Wang, Bin, et al.

        arXiv preprint arXiv:2304.13844 (2023).

      2. Deepsegmenter: Temporal action localization for detecting anomalies in untrimmed naturalistic driving videos

        Authors: Aboah, Armstrong, et al.

        Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.

        [Paper][Code]

      2. Real-time multi-class helmet violation detection using few-shot data sampling technique and yolov8

        Authors: Aboah, Armstrong, et al.

        Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.

        [Paper][Code]

      2. GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection

        Authors: Jha, Debesh, et al.

        ICML Workshop (2023).

        [Paper][Code]

      2. Gastrointestinal Disease Diagnosis with Hybrid Model of Capsules and CNNs

        Authors: Sarsengeldin, Merey, et al.

        2023 IEEE International Conference on Electro Information Technology (EIT).

        [Paper]

      2. Radiomics Boosts Deep Learning Model for IPMN Classification

        Authors: Yao, Lanhong, et al

        MICCAI MLMI 2023.

        [Paper]

      2. Monkeypox Diagnosis with Interpretable Deep Learning

        Authors: Ahsan, Md Manjurul, et al.

        IEEE Access (2023)

        [Paper]

      2. A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging

        Authors: Yao, Lanhong, et al.

        Current Opinion in Gastroenterology 39.5 (2023): 436-447.

        [Paper]

      2. Deep Learning Algorithms for Pancreas Segmentation from Radiology Scans: A Review

        Authors: Zhang, Zheyuan, et al.

        Advances in Clinical Radiology (2023).

        [Paper]

      2. Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection

        Authors: Azad, Reza, et al

        MICCAI 2023.

        [Paper]

      2. Self-supervised Semantic Segmentation: Consistency over Transformation

        Authors: S Karimijafarbigloo, R Azad, et al.

        ICCV 2023, IEEE International Conference on Computer Vision 2023

        [Paper][Code]

        2. Design and Rationale for the Use of Magnetic Resonance Imaging Biomarkers to Predict Diabetes After Acute Pancreatitis in the Diabetes Related to Acute Pancreatitis and Its Mechanisms Study: From the Type 1 Diabetes in Acute Pancreatitis Consortium

          Authors: Tirkes T., et al,

          Pancreas

          [Paper]

        2. Multi-Institutional Large-Scale Validation of 8 Methods for Automatic Knee MRI Segmentation for Use in Clinical Trials

          Authors: Dam, E. B., et al.

          Osteoarthritis and Cartilage, 30, S291-S292 (2022).

          [Paper]

        2. Deep Learning Models in the Classification of Intraductal Papillary Mucinous Neoplasms with Visual Explanation Methods to Highlight Areas of Interest for Algorithm Decision Process

          Authors: Engels, M. M., et al.

          Gastroenterology, 162(7), S-127 (2022).

          [Paper]

        2. Enhancing organ at risk segmentation with improved deep neural networks

          Authors: Isler, Ilkin, et al.

          In Medical Imaging 2022: Image Processing (Vol. 12032, pp. 814-820) (2022).

          [Paper]

        2. Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem

          Authors: Marvasti, Amir Emad, et al.

          2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME).

          [Paper]

        2. Detecting COVID-19 from respiratory sound recordings with transformers

          Authors: Aytekin, Idil, et al.

          In Medical Imaging 2022: Computer-Aided Diagnosis (Vol. 12033, pp. 25-32) (2022)

          [Paper]

        2. TGANet: Text-guided attention for improved polyp segmentation

          Authors: Tomar, N. K., Jha, D., Bagci, U., & Ali, S.

          MICCAI 2022 (2022).

          [Paper]

        2. Transformer-based Generative Adversarial Network for Liver Segmentation

          Authors: Demir, Ugur, et al.

          2022 International Conference on Image Analysis and Processing (pp 340–347).

          [Paper]
        1. Dynamic Linear Transformer for 3D Biomedical Image Segmentation

        2. Dynamic Linear Transformer for 3D Biomedical Image Segmentation

          Authors: Zhang, Z., & Bagci, U.

          2022 International Workshop on Machine Learning in Medical Imaging (pp 171–180).

          [Paper]

        2. Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network

          Authors: Tomar, Nikhil Kumar, et al.

          2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS).

          [Paper]
        1. Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network

        2. Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network

          Authors: Srivastava, Abhishek, et al.

          2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS).

          [Paper]

        2. TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation

          Authors: Tomar, Nikhil Kumar, et al.

          arXiv preprint arXiv:2206.08985 (2023).

          [Paper]
        1. Video Analytics in Elite Soccer: A Distributed Computing Perspective

        2. Video Analytics in Elite Soccer: A Distributed Computing Perspective

          Authors: Jha, Debesh, et al.

          In 2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM) (pp. 221-225) (2022

          [Paper]

        2. Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images

          Authors: Proietto Salanitri, F. et al.

          Proietto Salanitri, F. et al.

          [Paper]

        2. Multi-Contrast MRI Segmentation Trained on Synthetic Images

          Authors: Irmakci, I., Unel, Z. E., Ikizler-Cinbis, N., & Bagci, U.

          IEEE EMBC 2022.

          [Paper]

        2. COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformers

          Authors: Aytekin, Idil, et al.

          IEEE JBHI (2022).

          [Paper]

        2. Musculoskeletal MR Image Segmentation with Artificial Intelligence

          Authors: Keles, E., Irmakci, I., Bagci, U.

          Advances in Clinical Radiology, 4(1), 179-188 (2022).

          [Paper]

        2. An automatic segmentation framework for computer-assisted renal scintigraphy

          Authors: Rahimi, A., Hosntalab, M., Babapour, F., Amoui, M., Bagci, U.

          Medical & Biological Engineering & Computing, 1-11 (2022).

          [Paper]

        1. Missed Diagnosis of Pancreatic Ductal Adenocarcinoma Detection Using Deep Convolutional Neural Network.

          Authors: Hoogenboom, S. A., Ravi, K., Engels, M. M., Irmakci, I., Keles, E., Bolan, C. W., … & Bagci, U. (2021).

          Gastroenterology, 160(6), S-18.

          [Paper]

        2. Deep Recurrent-Convolutional Model for Automated Segmentation of Craniomaxillofacial CT Scans.

          Authors: Murabito, Francesca, Simone Palazzo, F. Proietto Salanitri, et al.

          In 2020 25th International Conference on Pattern Recognition (ICPR), pp. 9062-9067. IEEE, 2021.

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        3. Deep Multi-stage Model for Automated Landmarking of Craniomaxillofacial CT Scans.

          Authors: Palazzo, Simone, Giovanni Bellitto, Luca Prezzavento, et al.

          In 2020 25th International Conference on Pattern Recognition (ICPR), pp. 9982-9987. IEEE, 2021.

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        4. Interpretable Deep Model for Predicting Gene-Addicted Non-Small-Cell Lung Cancer in CT Scans.

          Authors: Pino, Carmelo, Simone Palazzo, Francesca Trenta, et al.

          In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 891-894. IEEE, 2021.

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        5. No-Reference Image Quality Assessment Of T2-Weighted Magnetic Resonance Images In Prostate Cancer Patients.

          Authors: Masoudi, S., Harmon, S., Mehralivand, S., Lay, N., Bagci, U., Wood, B.J., Pinto, P.A., Choyke, P. and Turkbey, B. (2021).

          In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) (pp. 1201-1205). IEEE.

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        6. Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis.

          Authors: Demir, U., Irmakci, I., Keles, E., Topcu, A., Xu, Z., Spampinato, C., … & Bagci, U. (2021).

          In International Workshop on Machine Learning in Medical Imaging (pp. 396-405). Springer, Cham.

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        7. Hierarchical 3D Feature Learning for Pancreas Segmentation.

          Authors: Proietto Salanitri, F., Bellitto, G., Irmakci, I., Palazzo, S., Bagci, U., & Spampinato, C. (2021).

          n International Workshop on Machine Learning in Medical Imaging (pp. 238-247). Springer, Cham.

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        8. Predicting RF Heating of Conductive Leads During Magnetic Resonance Imaging at 1.5 T: A Machine Learning Approach.

          Authors: Zheng, C., Chen, X., Nguyen, B. T., et al. (2021).

          n 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 4204-4208). IEEE.

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        9. Quick Guide on Radiology Image Pre-processing for Deep Learning Applications in Prostate Cancer Research.

          Authors: Masoudi, Samira, Stephanie AA Harmon, Sherif Mehralivand, et al.

          Journal of Medical Imaging 8, no. 1 (2021): 010901.

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        10. Capsules for Biomedical Image Segmentation.

          Authors: LaLonde, R., Xu, Z., Irmakci, I., Jain, S., & Bagci, U. (2021)

          Medical image analysis, 68, 101889.

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        11. A Machine Learning-Based Prediction of the Micropapillary/Solid Growth Pattern in Invasive Lung Adenocarcinoma with Radiomics.

          Authors: He, B., Song, Y., Wang, L., et al. (2021).

          Translational Lung Cancer Research, 10(2), p.955.

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        12. The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset.

          Authors: Desai, A. D., Caliva, F., Iriondo,

          C., et al. (2021). Radiology: Artificial Intelligence, 3(3), e200078.

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        13. Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players.

          Authors: RaviPrakash, H., Anwar, S. M., Biassou, N. M., & Bagci, U. (2021).

          Frontiers in Neuroscience, 15, 629478.

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        14. Deep Learning Based Staging of Bone Lesions from Computed Tomography Scans.

          Authors: Masoudi, S., Mehralivand, S., Harmon, S.A., et al. (2021).

          IEEE Access, 9, pp.87531-87542.

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        15. Attention-guided Analysis of Infrastructure Damage with Semi-supervised Deep Learning.

          Authors: Karaaslan, E., Bagci, U., & Catbas, F. N. (2021).

          Automation in Construction, 125, 103634.

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        16. Maximum Probability Theorem: A Framework for Probabilistic Machine Learning.

          Authors: Marvasti, A. E., Marvasti, E. E., Bagci, U., & Foroosh, H. (2021).

          IEEE Transactions on Artificial Intelligence, 2(3), 214-227.

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        17. A Novel Decision Support System for Long-Term Management of Bridge Networks.

          Authors: Karaaslan, E., Bagci, U., & Catbas, N. (2021). Applied Sciences, 11(13), 5928.

          Applied Sciences, 11(13), 5928.

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        18. Machine Learning-Based Prediction of MRI-Induced Power Absorption in the Tissue in Patients With Simplified Deep Brain Stimulation Lead Models

          Authors: Vu, J., Nguyen, B. T., Bhusal, B., Baraboo, J., Rosenow, J., Bagci, U., … & Golestanirad, L. (2021).

          IEEE Transactions on Electromagnetic Compatibility, 63(5), 1757-1766.

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        19. The Role of CO-RADS Scoring System in the Diagnosis of COVID-19 Infection and its Correlation with Clinical Signs

          Authors: Çomoglu, S., Öztürk, S., Topçu, A., Kulali, F., Kant, A., Sobay, R., … & Yilmaz, G. Current

          Medical Imaging 18.4 (2022): 381-386.

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        20. Adipose Tissue Segmentation in Unlabeled Abdomen MRI Using Cross Modality Domain Adaptation.

          Authors: Masoudi, S., Razi, A., Gatlin, J., Turkbey, B., Bagci, U.

          IEEE EMBC 2020.

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        21. Variational Capsule Encoder.

          Authors: RaviPrakash, H., Anwar, SM., Bousquet, C., Bagci, U.

          ICPR 2020.

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        22. Instance-level Microtubule Tracking.

          Authors: Masoodi, S., Razi, A., Wright, C., Gatlin, J., Bagci, U.

          IEEE Transactions on Medical Imaging

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        23. Diagnosing Colorectal Polyps in the Wild with Capsule Networks.

          Authors: LaLonde, R., Kandel, P., Spampinato, C., Wallace, M, Bagci, U.

          IEEE ISBI 2020.

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        24. Brain Tumor Survival Prediction using Radiomics Features.

          Authors: Yousaf, S., Anwar, S, RaviPrakash, H., Bagci, U.

          RNO-AI (Radiomics and Radiogenomics in Neuro-oncology), MICCAI 2020.

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        25. Overall Survival Prediction in Gliomas Using Region-Specific Radiomic Features.

          Authors: Shaheen, A., Bagci, U., Mohy-ud-Din, H.

          RNO-AI (Radiomics and Radiogenomics in Neuro-oncology), MICCAI 2020.

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        26. Encoding High-Level Visual Attributes in Capsules for Explainable Medical Diagnoses.

          Authors: LaLonde, R., Torigian, D., Bagci, U.

          MICCAI 2020.

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        27. Deep Convolutional Neural Networks Based Classification of Alzheimer’s Disease Using MRI Data.

          Authors: Nawaz, A., Anwar, S., Liaqat, R., Iqbal, J., Bagci, U.

          IEEE INMIC 2020.

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        28. State-of-the-art in brain tumor segmentation and current challenges.

          Authors: Yousaf, Sobia, Harish RaviPrakash, Syed Muhammad Anwar, Nosheen Sohail, and Ulas Bagci.

          n Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-Oncology, pp. 189-198. Springer, Cham, 2020.

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        29. Deep Learning Provides Exceptional Accuracy to ECoG-based Functional Language Mapping for Epilepsy Surgery.

          Authors: RaviPrakash, H., Korostenskaja, M., Castillo, E., Lee, KH., Salinas, CM., Baumgartner, J., Scampinatoe, C., Bagci, U.

          Frontiers in Neuroscience.

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        30. Analysis of Video Retinal Angiography with Deep Learning and Eulerian Magnification.

          Authors: Saha, S., LaLonde R., Carmack, A., Foroosh, H., Olson, J.C., Shaikh, S., Bagci, U.

          Frontiers in Computer Science.

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        31. AI in Gastroenterology. Current State of Play and Potential. How will it affect our practice and when?

          Authors: Hoogenboom, S., Bagci, U., Wallace, MB.

          Techniques in Gastrointestinal Endoscopy.

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        32. PET/CT and PET/MRI in Ophthalmic Oncology.

          Authors: Kalemaki MS, Karantanas AH, Exarchos D, Detorakis ET, Zoras O, Marias K, Millo C, Bagci U, Pallikaris I, Stratis A, Karatzanis I, Perisinakis K, Koutentakis P, Kontadakis GA, Spandidos DA, Tsatsakis A, Papadakis GZ.

          International Journal of Oncology.

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        33. Development of a Device-to-Image Registration Free Needle Guide for Magnetic Resonance Imaging-Guided Targeted Prostate Biopsy.

          Authors: Pankaj Kulkarni, Sakura Sikander, Pradipta Biswas, Sumit Laha, Heather Cornnell, Jeremy R Burt, Ulas Bagci, Sang-Eun Song.

          Journal of Medical Devices 14(4).

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        34. EEG based classification of long-term stress using psychological labeling.

          Authors: Saeed, Sanay Muhammad Umar, Syed Muhammad Anwar, Humaira Khalid, Muhammad Majid, and Ulas Bagci.

          Sensors 20, no. 7 (2020): 1886.

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        35. Proceedings from the first global artificial intelligence in gastroenterology and endoscopy Summit.

          Authors: Parasa, S., Wallace, M., Bagci, U., Antonino, M., Berzin, T., Byrne, M., Celik, H., Farahani, K., Golding, M., Gross, S., and Jamali, V.

          Gastrointestinal endoscopy, 92(4), pp.938-945.

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        36. The impact of COVID-19 on African American communities in the United States.

          Authors: Cyrus, Elena, Rachel Clarke, Dexter Hadley, Zoran Bursac, Mary Jo Trepka, Jessy G. Dévieux, Ulas Bagci

          Health Equity 4, no. 1 (2020): 476-483.

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        37. Integrating eye tracking and speech recognition accurately annotates MR brain images for deep learning: proof of principle.

          Authors: Stember, Joseph N., Haydar Celik, David Gutman, Nathaniel Swinburne, Robert Young, Sarah Eskreis-Winkler, Andrei Holodny

          Radiology: Artificial Intelligence 3, no. 1 (2020)

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        38. Semi-supervised deep learning for multi-tissue segmentation from multi-contrast MRI.

          Authors: Anwar, Syed Muhammad, Ismail Irmakci, Drew A. Torigian, Sachin Jambawalikar, Georgios Z. Papadakis, Can Akgun, Jutta Ellermann, Mehmet Akcakaya, and Ulas Bagci.

          Journal of Signal Processing Systems (2020)

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        39. AI for the detection of COVID19 pneumonia on chest CT using multinational datasets.

          Authors: Stephanie A. Harmon, Thomas H. Sanford, Sheng Xu, et al.

          Nature Communications, 2020.

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        40. Prognostic Utility of F-18-NaF PET/CT Imaging for Fractures in Patients with Fibrous Dysplasia of Bone.

          Authors: Papadakis, GZ., Manikis, GC, Karantanas, AH., Marias, K., Bagci, U., Florenzano, P., Collins, MT, Boyce, AM.

          EJNMMI 2019.

        41. F-18-NaF Uptake by Fibrous Dysplasia Bone Lesions is Positively Associated with Bone Turnover Markers (BTMs).

          Authors: Papadakis, GZ., Manikis, GC., Karantanas, AH., Marias, K., Bagci, U., Florenzano, P., Collins, MT, Boyce, AM.

          EJNMMI 2019.

        42. Colorectcal Polyp Diagnosis With Contemporary Artificial Intelligence.

          Authors: Kandel, P., Lalonde, R., Ciofoaia, V., Wallace, M., Bagci, U.

          Gastrointestinal Endoscopy.

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        43. Brown Adipose Tissue Activation Detected by Artificial Intelligence Assisted Radiomics: An Early Biomarker for Pancreatic Ductal Adenocarcinoma (PDAC).

          Authors: Rodriguez, AC., Sharma, A., Tanner, I., Kandel, P., Lalonde, R., Livingston, D., Manoj, J., Raimondo, M., Wallace, M., Bagci, U.

          Gastroenterology.

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        44. MRI-guided, Transperineal Prostate Biopsy Using Fixed Coordinate Needle Guide: Initial Feasibility Study.

          Authors: Kulkarni, P., Laha, S., Sikander, S., Cornell, H., Bagci, U., Burt, J., and Song, SS.

          Design of Medical Devices Conference, 2019.

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        45. Deep Learning for Musculoskeletal Image Analysis.

          Authors: Irmakci, I., Anwar, S., Torigian, D., Bagci, U.

          IEEE ASILOMAR 2019 (invited article).

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        46. Emotion Classification in Response to Tactile Enhanced Multimedia Using Frequency Domain Features of Brain Signals.

          Authors: Arsalan, A., Majid, M., Anwar, SM., Bagci, U.

          IEEE EMBC.

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        47. Classification of Perceived Human Stress Using Psychological Signals.

          Authors: Arsalan, A., Majid, M., Anwar, SM., Bagci, U.

          IEEE EMBC.

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        48. A Survey on Recent Advancements for AI-Enabled Radiomics in Neuro-Oncology.

          Authors: Anwar, S., Tooba, A., Rafique, K., RaviPrakash, H., Mohy-ud-din, H., Bagci, U.

          MICCAI 2019-RNO-AI.

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        49. Cross-modality Knowledge Transfer for Prostate Segmentation from CT Scans.

          Authors: Liu, Y., Khosravan, N., Liu, Y., Stember, J., Bagci, U., Jambawalikar, S.

          MICCAI 2019-DART.

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        50. Weakly Supervised Segmentation by A Deep Geodesic Prior.

          Authors: Mortazi, A., Khosravan, N., Torigian, DA., Kurugol, S., Bagci, U.

          MICCAI 2019-MLMI.

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        51. PAN: Projective Adversarial Network for Medical Image Segmentation.

          Authors: Khosravan, N., Mortazi, A., Wallace, M., Bagci, U.

          MICCAI 2019.

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        52. INN: Inflated Neural Networks for IPMN Diagnosis.

          Authors: LaLonde, R., Tanner, I., Nikiforaki, K., Papadakis, GZ., Kandel, P., Bolan, CW., Wallace, M., Bagci, U.

          MICCAI 2019.

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        53. Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches.

          Authors: Hussein, S., Kandel, MM., Bolan, CW., Wallace, MB., Bagci, U.

          IEEE Transactions on Medical Imaging, 2019.

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        54. A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye Tracking, Sparse Attentional Model, and Deep Learning.

          Authors: Khosravan, N., Celik, H., Turkbey, B., Jones, E., Wood, B., Bagci, U.

          Medical Image Analysis, 2019.

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        55. Deep Geodesic Learning for Segmentation and Anatomical Landmarking.

          Authors: Torosdagli, D. Liberton, … , Bagci, U.

          IEEE Transactions on Medical Imaging, 2019.

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        56. Artificial Intelligence Assisted Infrastructure Assessment Using Mixed Reality Systems.

          Authors: Karaaslan, E., Bagci, U., and Catbas, F.N.

          Journal of Transportation Research, 2019.

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        57. Eye-Tracking for Deep Learning Segmentation Using Convolutional Neural Networks: a proof-of-principle application to meningiomas.

          Authors: Stember, JN., … , Jambawalikar, S., Bagci, U.

          Journal of Digital Imaging, 2019.

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        58. 18F-NaF PET/CT imaging in fibrous dysplasia of bone: implications for evaluation of disease activity and treatment.

          Authors: Papadakis, GZ., … , and Boyce, AM.

          Journal of Bone and Mineral Research, 2019.

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        59. Deep Learning to Classify Intraductal Papillary Mucinous Neoplasms Using Magnetic Resonance Imaging.

          Authors: Hurtado, S. Hussein, … , and M. Wallace.

          Pancreas, 2019.

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        60. Quality Assurance of Computer-Aided Detection and Diagnosis in Colonoscopy.

          Authors: Vinsard, DG., … , Wallace, MB.

          Gastrointestinal Endoscopy, 2019.

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        61. RETOUCH-The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge.

          Authors: Bugunovic, H., … , Gerendas, BS., Klaver, C., Sánchez, CI., Schmidt-Erfurth, U.

          IEEE Transactions on Medical Imaging, 2019.

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        62. Computerized Analysis of Brain MR parameters dynamics in young patients with Cushing-Syndrome – a case control study.

          Authors: Tirosh, A., RaviPrakash, H., … , Bagci, U., Stratakis, CA.

          The Journal of Clinical Endocrinology and Metabolism, 2019.

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        63. Lung Imaging and CADx. Chapter 14: How Deep Learning is Changing the Landscape of Lung Cancer Diagnosis.

          Authors: Hussein, S., and Bagci, U.

          CRC Press (invited), 2019.

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        64. Deep Learning for Functional Brain Connectivity: Are We There Yet?

          Authors: RaviPrakash, R., Watane, A., Jambawalikar, S., and Bagci, U.

          Springer (Deep Learning and CNN for Medical Image Computing, Invited), 2019.

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        65. Gold Standard for Epilepsy/Tumor Surgery Coupled with Deep Learning Offers Independence to a Promising Functional Mapping Modality.

          Authors: Korostenskaja, M., RaviPrakash, H., Bagci, U., … , Elsayed, M., and Castillo, E.

          Brain-Computer Interface Research, A State-of-the-Art Summary 7, Springer, 2019.

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        66. Artificial Intelligence as Another Set of Eyes in Breast Cancer Diagnosis.

          Authors: Anwar, SM, and Bagci, U.

          Journal of Medical Artificial Intelligence, 2019.

          [Paper]

          67. Leave A Message

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