Bagci Lab
Search

News & Events

April 1, 2024

 
 

We have two abstract accepted for cardiothoracic research at ESC and AHA.

Congrats to Vedat CIcek and the team!

1. A review of prognostic prediction of coronary artery disease patients with myocardial perfusion scintigraphy and artificial intelligence.
2. Predicting Short Term Mortality In Patients With Acute Pulmonary Embolism With Deep Learning.
 

April 1, 2024

 
 

We have an abstract accepted at ISMRM 2024!

Congratulations to Mohamed Elbayumi  for this great work!

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

Mar 26, 2024

 
 

Three abstracts are accepted at the World's premier gathering of gastroenterologists, researchers and industry partners, and we also obtained one distinction poster prize!!

Congratulations to our lab members! The following three papers were accepted for publication in  DDW 2024:

1. ENHANCING LIVER SEGMENTATION OUTCOMES WITH MSFORMER-BASED ARTIFICIAL INTELLIGENCE SYSTEM.
https://arxiv.org/abs/
2.  THE BOSTON ERCP DATASET: A VIDEO DATASET FOR ADVANCED ENDOSCOPY. [Distinction Poster Prize]
https://arxiv.org/abs/
3.  ENHANCING COLONOSCOPY OUTCOMES WITH DAPODET-BASED AI FOR REAL-TIME SESSILE SERRERATED POLYP DETECTION
https://arxiv.org/abs/
 

Mar 12, 2024

 
 

Advancements in Pancreatic Cancer Detection: Collaboration Between Bagci's Lab and Mayo Clinic!!

Our joint partnership between the Machine and Hybrid Intelligence at Northwestern University and the Mayo Clinic Pancreas Cancer Early Detection team of Michael B Wallace continues to produce major advances toward our goal of detecting the earliest and treatable forms of pancreatic cancer. Recent breakthroughs include highly reliable systems for pancreatic segmentation on CT and MRI, which should improve the detection of very early malignant change and solid and cystic pancreatic tumors.

Mar 9, 2024



Bagci lab is presenting six papers in IEEE ISBI 2024 which will be held in Athens, Greece this year!!

Congratulations to our lab members! The following six papers were accepted for publication in IEEE ISBI 2024
1. Leveraging Unlabeled Data for 3D Medical Image Segmentation through Self-Supervised Contrastive Learning.
2.  Explainable Transformer Prototypes for Medical Diagnoses
3.  FuseNet: Self-Supervised Dual-Path Network for Medical Image Segmentation
4. HCA-Net: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling
5. CT Liver Segmentation via PVT-based Encoding and Refined Decoding
6.  Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor Segmentation.
 

Feb 23, 2024


Latest publication! 'Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions' has been published in IEEE Internet of Things Journal!

This article outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of FL, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. Recent literature has shown that FL models are robust and generalize well to new data, which is essential for medical applications.

paper link: https://ieeexplore.ieee.org/document/10304218

Feb 18, 2024


Latest publication alert! 'Artificial Intelligence and Infectious Disease Imaging' has been published in The Journal of Infectious Diseases!

The COVID-19 pandemic, alongside the widespread availability of graphics processing units, has accelerated advancements in AI and medical imaging, offering new avenues to enhance patient care despite initial challenges in model performance on novel data. These developments not only promise to improve future medical AI applications but also underscore the potential of AI in infectious disease imaging research, addressing evidence gaps and fostering innovative solutions in the field.

paper link: https://academic.oup.com/jid/article/228

Feb 18, 2024


Our new paper, "Domain Generalization with Fourier Transform and Soft Thresholding," has been accepted at IEEE ICASSP 2024!

Our research advances domain generalization by integrating soft-thresholding with Fourier-transform techniques, reducing background interference in retinal fundus image segmentation. This method, proven on public datasets, markedly enhances model generalization and accuracy, surpassing current standards.

paper link: https://arxiv.org/html/2309.09866v3

Feb 18, 2024


Explore our latest work in Earth Science Informatics: AI-Powered Road Network Prediction with Satellite Imagery and GPS Trajectory - A novel method for enhanced road network mapping

This study presents an innovative approach for automatic road detection with deep learning, employing fusion strategies to utilize both lower-resolution satellite imagery and GPS trajectory data, a concept never explored before.

paper link: https://link.springer.com/article/10.1007/s12145-023-01201-6

Feb 17, 2024

 

Congrats to the whole team! We have published a new paper in Revista de investigación clínica

The aim of this study was to investigate the prognostic relevance of PIV in predicting in-hospital mortality in acute pulmonary embolism (PE) patients and to compare it with the well-known risk scoring system, PE severity index (PESI), which is commonly used for a short-term mortality prediction in such patients.

paper link: https://pubmed.ncbi.nlm.nih.gov/38359843/

Sep 07, 2023

 

Congrats! Our GazeSAM work is accepted in NeurIPS 2023 Workshop Gaze Meets ML

This study introduces eye gaze as a novel interactive prompt for image segmentation, different than previous model-based applications. Specifically, leveraging the real-time interactive prompting feature of the recently proposed Segment Anything Model (SAM), we present the GazeSAM system to enable users to collect target segmentation masks by simply looking at the region of interest. 

Github Page: https://ukaukaaaa.github.io/projects/gazesam/gazesam.html
paper link: https://openreview.net/pdf?id=hJ5DREWdjs

 

Sep 07, 2023

 

A Recently Released Multi-Center Gastrointestinal Tract Dataset with 8K Images

We recently released a multi-center gastrointestinal tract dataset with 8K images from 27 classes. We hope that it opens new possibilities in GI endoscopy & colonoscopy research.

Dataset link: https://osf.io/84e7f/
paper link: https://arxiv.org/pdf/2307.08140.pdf

Sep 03, 2023

 

We Have 11 Posters to Present in The 17th Annual Lewis Landsberg Research Day

The annual Research Day at Northwestern is a campuswide event to promote faculty and trainee development by sharing exciting research and conversations with colleagues. This year we have 11 posters to present:

  1. GazeSAM: What You See is What You Segment
  2. Deep Learning Algorithm for Accurate and Real-time Liver Segmentation
  3. Visual Explanations for Radiology AI Applications with Information Bottleneck
  4. Ensemble learning with Residual Transformer for Brian Tumor Segmentation
  5. Monkeypox Diagnosis With Interpretable Deep Learning
  6. A Fully Automatic AI System for Pancreas Segmentation from
    Multicenter MRI Scans
  7. AI-Based Pancreatic Cancer Risk Classification in IPMN Patients
  8. Fast Forward Forward: Analytic Training for Deep Neural Networks
  9. Computer-Aided Automatic Measurement of Pancreatic Cysts
  10. Cognitive Perspectives on Complexity of Diagnostic Search and Its Analogy in Artificial Intelligence
  11. Pancreatic Cancer: Decoding Risks with Duct Diameter, Cyst Size, and Abdominal Circumference

Congrats!

 

Aug 25, 2023

 

Prof. Bagci Gave an Invited Talk Titled “Artificial Intelligence in Medical Imaging"

At the International Symposium on Current Advances in Medical Imaging Techniques, Prof. Bagci highlighted our recent findings from several high-risk, high-gain projects, funded by NIH.

Aug 20, 2023

 

Congrats! Two papers are accepted in WACV2024

Two papers got accepted by Winter Conference on Applications of Computer Vision (WACV2024). 

Paper #1: GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification
Paper #2: Domain Generalization with Correlated Style Uncertainty
 
Congrats to all students, postdocs and collaborators for these two amazing studies!

WACV is a premier venue for computer vision and machine learning research. Read more details at:

 

Read More >>

 

 

 

Aug 15, 2023

 

Spotlight in NU's Newsletter

Our lab MHIL was recently featured in Northwestern Medicine’s newsletter, highlighting progress in AI for Pancreatic Diseases.  Read more details at:

 

Aug 12, 2023

 

Congrats! Our Papers are Accepted in ICCV CVAMD 2023

We have published an ICCV CVAMD 2023 paper! ICCV is a premier venue for computer vision and machine learning and the paper detail is the following:
 
Self-supervised Semantic Segmentation: Consistency over Transformation
 S Karimijafarbigloo, R Azad, et al. ICCV 2023

Congrats to Sanaz Karimijafarbigloo and Reza Azad for this great work!

Aug 12, 2023

 

Congrats! 3 Papers in MICCAI 2023

We have published two MICCAI 2023 papers and a MICCAI MLMI workshop paper! MICCAI (https://conferences.miccai.org/2023/en/) will be held in Vancouver, Canada this year and considered as the top medical AI conference in the world. Here are the papers’ details:
 

Paper #1: A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications.  
First author: Matteo Pennisi

Paper #2: Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection.  (link to be published)
First author: Reza Azad.
arXiv:2309.00108 (2023).

Paper #3: Radiomics boosts deep learning in Pancreas IPMN Classification.  (link to be published)
First author: Lanhong Yao
http://arxiv.org/abs/2309.05857

Congrats to all students, postdocs and collaborators for these two amazing studies!  

 

Aug 09, 2023

 

Dr. Bagci Joins Renowned Panel on Machine Learning for Multimodal Healthcare Data at ICML 2023

We’re proud to announce that Dr. Bagci represented our lab as a panelist in the ML4MHD (Machine Learning for Multimodal Healthcare Data) session at ICML 2023. Chaired by Dr. Pallavi Tiwari, the panel also featured esteemed experts like Dr. Daniel Rueckert and Dr. Xiaoxiao Li.

Aug 09, 2023

 

New Paper is Accepted in ICML

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

by Debesh J. et al.

 

Aug 08, 2023

 

Pioneering AI for Nuclear Medicine Imaging: Seminar with Dr. Bo Zhou of Yale University

We were honored to host Dr. Bo Zhou, a distinguished final-year Ph.D. Candidate from the Department of Biomedical Engineering at Yale University, for a captivating seminar on AI applications in Multi-modal Nuclear Medicine (NM) Imaging. Dr. Zhou’s extensive research addresses the critical challenges of high radiation doses, image quality degradation, and prolonged acquisition times in imaging modalities like PET-CT and PET-MRI. His innovative deep-learning strategies aim to enhance the safety, efficiency, and quality of these imaging techniques, potentially elevating their clinical utility. Dr. Zhou, with a commendable academic and research background marked by awards and over 50 publications, shared insights that promise to redefine the boundaries of medical imaging. Explore more about Dr. Zhou’s work on his homepage.

Aug 03, 2023

 

Congrats to Harmony Presenting Insightful Study on Pancreatic Cancer Risks

Congratulations to our visiting student, Harmony, on her stellar presentation at Northwestern University! Her insightful study on “Pancreatic Cancer: Decoding Risks with Duct Diameter, Cyst Size, and Abdominal Circumference” showcased an in-depth analysis and a fresh perspective, further contributing to the evolving discourse on pancreatic cancer research. We’re proud of her achievements and commend her dedication to this vital field of study.

Aug 01, 2023

 

New NIH U01 Grant from NCI

Dr. Bagci has received a prestigious NIH U01 grant from NCI, entitled “Hybrid Intelligence for Trustable Diagnosis for Prostate Cancer Diagnosis and Patient Management”.
 
Dr. Bagci will work with several world-renowned scientists and clinicians in this project including Dr. Turkbey (Co-PI), Dr. Pinto, and Dr Choyke of NIH, and Dr. Miller, Dr. Murphy, and Dr. Ross of Northwestern University. Dr Elif Keles of Bagci Lab will join the study as Co-I.

Jul 21, 2023

 

Empowering Women in Medical AI: A Showcase of Our Lab's Leading Innovations

On July 21st, our lab resonated with inspiration and forward-thinking, thanks to our remarkable women scientists at the forefront of medical imaging research. Their tales of dedication, challenges, and groundbreaking discoveries were the highlight of this special gathering.

Read More >>

 

Jun 22, 2023

 

Invited Talk at AI Summer School

Prof. Bagci gave an invited talk and lecture at the AI Summer School (Istanbul/Turkey),
called “Trustworhy AI for high Risk Applications”.
ddw

May 09, 2023

 

DDW 2023: Digestive Diseae Week

Congratulations to our team in DDW2023!

 

20230428_183737

Apr 28, 2023

 

Collaboration and Insight: Prof. Mubarak Shah Visits Our Lab

We are honored to welcome Dr. Mubarak Shah, a renowned professor from the University of Central Florida (UCF), to our lab. During his visit, we will share our latest research findings and engage in fruitful discussions to explore potential collaborations and future directions in our respective fields.

 

Aerial of campus at night.
Daniel Dubois/Vanderbilt University

July 10, 2023 (scheduled)

 

MIDL 2023 Paper is Accepted for Publication

We are excited to announce the acceptance of our MIDL 2023 paper for publication! Stay tuned for more details on the study.

ai in medicine

Mar 19, 2023 

 

"AI in Clinical Medicine" Now Available for Purchase

The new book co-edited by Prof. Bagci, “AI in Clinical Medicine: A Practical Guide for Healthcare Professionals,” is now available for purchase in both physical and electronic formats from Wiley. Get your copy at :

miccai2023-mobile-logo

Oct 8, 2023 (scheduled)

 

Keynote Speech on MICCAI 2023 Workshop

Prof. Bagci will be sharing expertise at the MICCAI 2023 workshop focusing on medical image learning with limited and noisy data. 

ICECCME-23

July 19, 2023

 

Keynote Speech on IEEE ICECCME 2023 Conference Agenda

A highly-anticipated keynote speech is set to be given at the upcoming IEEE ICECCME 2023 conference. It will focus on the failures of deep learning / AI algorithms and propose several approaches to increase robustness of AI powered medical imaging systems. 

 

upenn

Mar 16, 2023

 

Talk on Pancreatic Cyst Diagnosis with Explainable AI at the University of Pennsylvania

On March 16, 2023, an insightful talk was delivered at the University of Pennsylvania’s Radiology department, delving into the diagnosis and risk stratification of pancreatic cysts using explainable AI techniques.

 

 

Screen Shot 2023-04-19 at 1.18.46 AM

Feb 15, 2023

Trustworthy AI for Imaging-Based Diagnosis Presented at University of Wisconsin Madison

On February 15, 2023, an invited talk was given at the University of Wisconsin Madison’s BME department, highlighting the significance of trustworthy AI in imaging-based diagnosis.

Capsule

Jan 11, 2023

Research Team Secures Patent for Innovative Object Detection Algorithm: 'Deformable Capsules'

An improved method of performing object segmentation and classification that reduces the memory required to perform these tasks, while increasing predictive accuracy.

Jan 01, 2023

RSNA Grant to Learn Prolonged Symptoms of COVID-19

Bagci lab received a grant funded by RSNA Emerging Issues – Long-Term COVID Effects EILTC2208, entitled “PASC Pulmonary Fibrosis Prediction with Deep Learning and Multimodal Data.”

 

Dec 27, 2022

RSNA 2022 108th Scientific Assembly and Annual Meeting

Our team attended RSNA 2022 at Chicago. Ugur and Debesh are presenting our recent progress.

Creating reliable solutions for biomedical imaging.

Scroll to Top