Ulas Bagci

Associate Professor

Northwestern University

United States

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About

Ulas Bagci is an Associate Professor at Northwestern University, United States. His research primarily focuses on medical imaging, artificial intelligence in healthcare, and predictive modeling. Recent publications include work on enhancing medical image segmentation, unsupervised clustering for prognosis in cancer cohorts, and the integration of AI in managing pancreatic cystic lesions.

Articles (17)

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2025

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2020

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2020

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2020

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2020

Collaborators (10)

Cemal Yazici

Assistant Professor of Medicine

University of Chicago

UNITED STATES

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Mathias Perslev

Københavns Universitet

DENMARK

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Molly Bright

Assistant Professor

Northwestern University Feinberg School of Medicine

UNITED STATES

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Michael Wallace

Professor

Mayo Clinic

UNITED STATES

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Fikret Necati Catbas

Professor and Director

University of Central Florida

UNITED STATES

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Temel Tirkes

Indiana University School of Medicine

UNITED STATES

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Laleh Golestani Rad

Assistant Professor

Northwestern University

UNITED STATES

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Afsaneh Razi

Assistant Professor

Drexel University

UNITED STATES

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Kostas Perisinakis

University of Crete School of Medicine

GREECE

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Ronen Sumagin

Associate Professor

Northwestern University Feinberg School of Medicine

UNITED STATES

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Ulas Bagci

Research Interests

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Articles (17)

DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model

Unsupervised Clustering Successfully Predicts Prognosis in NSCLC Brain Metastasis Cohorts

Paradoxical Response to Neoadjuvant Therapy in Undifferentiated Pleomorphic Sarcoma: Increased Tumor Size on MRI Associated with Favorable Pathology

Foundational artificial intelligence models and modern medical practice

Deformable Capsules for Object Detection

COVID-19 Detection From Respiratory Sounds With Hierarchical Spectrogram Transformers

Advances for Managing Pancreatic Cystic Lesions: Integrating Imaging and AI Innovations

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

Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI

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

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

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

Maximum Probability Theorem: A Framework for Probabilistic Machine Learning

PET/CT and PET/MRI in ophthalmic oncology (Review)

Instance-Level Microtubule Tracking

EEG Based Classification of Long-Term Stress Using Psychological Labeling

Computerized Analysis of Brain MRI Parameter Dynamics in Young Patients With Cushing Syndrome-A Case-Control Study

Collaborators (10)

Cemal Yazici

Mathias Perslev

Molly Bright

Michael Wallace

Fikret Necati Catbas

Temel Tirkes

Laleh Golestani Rad

Afsaneh Razi

Kostas Perisinakis

Ronen Sumagin

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