Foundation models like TITAN produce suboptimal embeddings for specialized clinical tasks such as breast cancer biomarker prediction. We propose a hierarchical refinement framework sequentially applying Direct Preference Optimization (DPO) and Supervised Contrastive Learning (SupCon) to enhance discriminative power. DPO adapts preference-based alignment to vision models through self-supervised construction of correction targets from misclassified samples, while SupCon leverages supervised labels to maximize class separability. Evaluated on TCGA-BRCA, our parameter-efficient approach achieves substantial improvements across balanced accuracy, Cohen’s κ, and AUROC metrics, demonstrating effective adaptation of foundation models for molecular biomarker classification in precision oncology.
Watch the team present this project at 01:45:27 in the session recording here.
Keywords: computational pathology, foundation models, direct preference optimization, supervised contrastive learning, biomarker prediction, breast cancer, whole slide imaging, TITAN, embedding refinement, precision oncology
Faculty Advisor
Dr. Pamuksuz, a professor of AI, specializes in applied mathematics, machine/deep learning, responsible and generative AI. He has contributed to various analytics journals, including IEEE Transactions on Artificial Intelligence, and has shared his insights at several national and international conferences.
Since joining the University of Chicago as a faculty member in 2018, Dr. Pamuksuz has taught a range of subjects, including data mining, machine learning, and linear & non-linear models, along with more specialized areas like AI-data science for leaders and Generative AI Research. His supervision of capstone theses has often centered on computer vision and natural language processing.
In the professional realm, Dr. Pamuksuz has an impressive record. He has led data science teams at several Fortune 500 companies, provided expert consultancy in architecting cloud-based machine learning solutions, and co-founded Inference Analytics in 2018. Under his leadership, Inference Analytics was recognized in 2023 as one of the top Machine Learning Companies in Illinois, marking a significant milestone in healthcare analytics in Chicago.
Dr. Pamuksuz’s academic path has taken him through some of Illinois’ most prestigious universities. He completed his MS in Computer Engineering/Science at Northwestern University and went on to earn his Ph.D. from UIUC. Today, he contributes his expertise as a faculty member at the University of Chicago. This journey, connecting three significant academic institutions, reflects a strong foundation and dedication to his field and a deep engagement with the state’s rich educational landscape. Outside of his professional endeavors, Dr. Pamuksuz is enthusiastic about hackathons, not only participating in several but also organizing them twice annually since 2020. His involvement has yielded notable success in various data challenges. As a sports fan, he follows Champions League Soccer, supporting Galatasaray, and enjoys engaging in basketball and volleyball during his leisure time. He also enjoys smooth/gypsy jazz where you can find him on Wednesdays in his favorite place Green Mill.