Bagci Lab



Machine and Hybrid Intelligence Lab (MHIL) aims to accelerate research in the machine learning field to develop solutions for high-risk artificial intelligence (AI) problems.


Research in Biomedical Imaging Analysis

Key Projects

AI for Pancreatic Diseases

Capsule Networks

Eye Tracking

Research in Biomedical Imaging Analysis

Application Highlights

Cancer Detection

TRUPNet: Transformer based Residual Upsampling Network for Real-Time Polyp Segmentation


Musculoskeletal MRI Image Segmentation with Artificial Intelligence

Risk Management

Overall Survival Prediction of Glioma Patients With Multiregional Radiomics

Eye tracking

GazeGNN: A Gaze-Guided Graph Neural Network for Disease Classification

Research in Biomedical Imaging Analysis

Funding Resources

NIH/NCI #U01CA268808-01A1

Hybrid Intelligence for Trustable Diagnosis for Prostate Cancer Diagnosis and Patient Management (HIT-PIRADS)

To create a new AI-driven diagnosis and patient management system that will increase positive predictive value for clinically significant prostate cancer detection while minimizing unnecessary prostate biopsies and related morbidities.

RSNA Emerging Issues – Long-Term COVID Effects EILTC2208

PASC Pulmonary Fibrosis Prediction with Deep Learning and Multimodal Data

To learn prolonged symptoms of COVID-19.

NIH/NIDDK #U01 DK127384-02S1   

Imaging Morphology of Pancreas in Diabetic Patients following Patients following Acute Pancreatitis (IMMINENT) Study

Data Coordinating Center for the Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC).

NIH/NIBIB #R03 EB032943-02

Application of machine learning for fast prediction of MRI-induced RF heating in patients with implanted conductive leads

Our work will introduce a paradigm shift in the practice of MRI RF heating assessment, reducing simulation times from tens of hours to a few minutes.

NIH/NCI #R01 CA246704-01



Cyst-X: Interpretable Deep Learning Based Risk Stratification of Pancreatic Cystic Tumors

The goal of this effort is to develop novel interpretable AI methods to determine the risk status of pre-cancerous pancreatic cysts at early stages using radiology screening (MRI).

NIH/NCI #R01 CA240639-01

Radiologist-Centered AI (RCAI) for Lung Cancer Screening and Diagnosis

The goal of this effort is to develop an AI system to integrate computer aided detection and diagnosis system with radiologists’ perceptive knowledge/pattern via eye-tracking.

Florida Department of Health #20K04

Predicting Outcomes of Lung Cancer Therapy Through Explainable Deep Learning

The goal of this project is to develop explainable deep learning algorithms for predicting radiation therapy outcome of patients diagnosed with lung cancer.

NIH #R15EB030356

Compact Registration-Free Robotic Tool Guide for Image-Guided Percutaneous Interventions.

The goal of the effort is to develop cutting edge robotic tool for image guided interventions in radiology without the use of registration operations.

Research in Biomedical Imaging Analysis


Capsules for image analysis
US US11010902B2 Ulas Bagci University Of Central Florida Research Foundation, Inc.
Priority 2018-06-04 • Filed 2019-06-04 • Granted 2021-05-18 • Published 2021-05-18

System and method for image-based quantification of white and brown adipose tissue at the whole body, organ and body-region levels
US US10157462B2 Ulas Bagci University Of Central Florida Research Foundation, Inc.
Priority 2016-06-27 • Filed 2017-06-27 • Granted 2018-12-18 • Published 2018-12-18

Eye tracking applications in computer aided diagnosis and image processing in radiology
US US10839520B2 Ulas Bagci The United States Of America, As Represented By The Secretary
Priority 2017-03-03 • Filed 2018-03-05 • Granted 2020-11-17 • Published 2020-11-17

Systems, methods, and media for automatically diagnosing intraductal papillary neoplasms using multi-modal magnetic resonance imaging data
US US11064902B2 Ulas Bagci Mayo Foundation For Medical Education And Research
Priority 2018-06-29 • Filed 2019-07-01 • Granted 2021-07-20 • Published 2021-07-20

Method for detection and diagnosis of lung and pancreatic cancers from imaging scans
US US20200160997A1 Ulas Bagci University Of Central Florida Research Foundation, Inc.
Priority 2018-11-02 • Filed 2019-11-04 • Published 2020-05-21

Deformable capsules for object detection
US US20210279881A1 Ulas Bagci University Of Central Florida Research Foundation, Inc.
Priority 2018-06-04 • Filed 2021-05-12 •Granted 2022-11-29• Published 2021-09-09

Methods of artificial intelligence-assisted infrastructure assessment using mixed reality systems
US US20210174492A1 Ulas Bagci University Of Central Florida Research Foundation, Inc.
Priority 2019-12-09 • Filed 2020-12-09 • Published 2021-06-10

Creating reliable solutions for biomedical imaging.

Scroll to Top