Machine and Hybrid Intelligence Lab
We study machine learning and human–AI collaboration for medical image analysis. Our research spans image segmentation, disease characterization, interpretable learning, and evaluation across clinical settings.
Northwestern University
Research areas
Research across medical imaging, machine learning, and the role of human expertise in clinical decisions.
We investigate how clinicians’ visual attention can inform image analysis, model training, and human–AI interaction.
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Research schematics illustrate the methods; primary sources are linked in each research area.
Selected publications
Selected publications from the lab and our collaborators.
Decentralized gossip learning and federated averaging for histopathology image classification
Neural Computing and Applications
Lab news
All news & updates
Outstanding Paper Award at Deep Breath 2026 / MICCAI
Our paper, “BreastMammo and DenseMammo: Benchmarks for Mammography Domain Generalization,” received the outstanding paper award.
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12 papers published at MICCAI 2026
2 main-conference papers · 10 workshop papers at MICCAI 2026, Strasbourg, France.
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Seven Abstracts Accepted at APA, Plus a Keynote on Clinical Translation of Pancreatic MRI AI
Advancing AI for pancreatic care - 7 abstracts, one shared goal!
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Principal investigator
Ulas Bagci, PhD
Dr. Bagci leads the Machine and Hybrid Intelligence Lab. His research examines artificial intelligence for medical image analysis, with an emphasis on interpretable methods and the integration of human expertise.
The lab brings together researchers in machine learning, medical imaging, and clinical science.
Research opportunities
Information for prospective doctoral researchers, postdoctoral fellows, and research collaborators.