MedAI #126: Divide & Conquer - Concept-based Models for Efficient Transfer Learning | Shantanu Ghosh

Title: Divide and Conquer: Carving Out Concept-based Models out of BlackBox for More Efficient Transfer Learning Speaker: Shantanu Ghosh Abstract: Building generalizable AI models is one of the primary challenges in the healthcare domain. While radiologists rely on generalizable descriptive rules of abnormality, Neural Networks (NN), often treated as blackboxes, suffer even with a slight shift in input distribution (e.g., scanner type). Fine-tuning a model to transfer knowledge from one domain to another requires a significant amount of labeled data in the target domain. In this paper, we develop a concept-based interpretable model that can be efficiently fine-tuned to an unseen target domain with minimal computational cost. We assume the interpretable component of NN to be approximately domain-invariant. Concept-based model design either starts with an interpretable design or a post-hoc-based approach from a Blackbox. Blackbox models are flexible but difficult to explain, while interpretable by-design models are inherently explainable. Yet, interpretable models require extensive machine learning knowledge and tend to be less flexible, potentially underperforming their Blackbox equivalents. My research aims to blur the distinction between a post-hoc explanation of a Blackbox and constructing interpretable models. In the first part of the talk, beginning with a Blackbox, we iteratively carve out a mixture of concept-based interpretable models and a residual network. The interpretable models identify a subset of samples and explain them using First Order Logic (FOL), providing basic reasoning on concepts from the Blackbox. We route the remaining samples through a flexible residual. We repeat the method on the residual network until all the interpretable models explain the desired proportion of data. In the second part of my talk, I will discuss an algorithm to transfer the interpretable models from a source domain to an unseen target domain with minimum training data and computation cost. Speaker Bio: Shantanu Ghosh is a PhD candidate in Electrical Engineering at Boston University, advised by Prof. Kayhan Batmanghelich. He completed his master's degree in Computer Science from the University of Florida under the supervision of Dr. Mattia Prosperi. His research interest lies in representation learning across computer vision and medical imaging, focusing on interpretability and explainable AI. Specifically, he investigates the representations learned across different modalities, architectures, and training strategies to enhance their generalizability, robustness, and trust. ------ The MedAI Group Exchange Sessions are a platform where we can critically examine key topics in AI and medicine, generate fresh ideas and discussion around their intersection and most importantly, learn from each other. We will be having weekly sessions where invited speakers will give a talk presenting their work followed by an interactive discussion and Q&A. Our sessions are held every Monday from 1pm-2pm PST. To get notifications about upcoming sessions, please join our mailing list: https://mailman.stanford.edu/mailman/listinfo/medai_announce For more details about MedAI, check out our website: https://medai.stanford.edu. You can follow us on Twitter @MedaiStanford Organized by members of the Rubin Lab (http://rubinlab.stanford.edu) and Machine Intelligence in Medicine and Imaging (MI-2) Lab: - Nandita Bhaskhar (https://www.stanford.edu/~nanbhas) - Amara Tariq (https://www.linkedin.com/in/amara-tariq-475815158/) - Avisha Das (https://dasavisha.github.io/)