AI and Multimodal Medical Imaging (AIMed) Lab
Overview
Welcome to the AI and Multimodal Medical Imaging (AIMed) Lab, led by Dr. Rakesh Shiradkar. Our research centers on discovering quantitative biomarkers from multi-modal medical imaging (spanning radiology and digital pathology) by leveraging cutting-edge computer vision, deep learning, and foundation model-based AI approaches.
We are dedicated to integrating diverse streams of medical data to design improved diagnostic, prognostic, and predictive tools for clinicians and patients, with the ultimate goal of advancing health outcomes.
As an interdisciplinary team of biomedical engineers, computer scientists, and physicians, we bring together students and professionals from varied backgrounds to push the boundaries of AI-driven medical imaging research.
Highlights
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PublicationNew publication out in JIIM
Sonawane S, Sompalle P, Baddula D, Kedare M, Setiadi N, Midya A, Azamat S, Zhang Z, Patil D, Yang A, Kikano E, Hill D, Massuti T, Sanda M, Madabhushi A, Bahler C, Shiradkar R. "Enhancing Robustness of Deep Learning to Batch Effects from Multi-site Data for Segmentation of Clinically Significant Prostate Cancer on MRI". Journal of Imaging Informatics in Medicine (2026).
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Award
UROP
Rushil Parikh, BS/MD student, awarded the UROP Scholarship. He will be working on building AI models for predicting response to radioligand therapies for prostate cancer.
Research Areas
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Pathology Informatics
Computational approaches leveraging digitized histopathology images to identify morphological biomarkers
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Radiology Informatics
Discovering quantitative imaging biomarkers from radiographic imaging (MRI, CT, X-rays) for improved diagnosis and prognosis
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Multimodal AI
Innovative approaches to integrate various medical imaging data streams
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Translational AI
Approaches to overcoming challenges for translating AI tools into clinical pipelines and workflows.