Bilkent University
Department of Computer Engineering
CS 590/690 SEMINAR
SAILR: Severity-Aware Multi-Label Whole Slide Image Retrieval via Attention-Driven Late Interaction
Sude Önder
Master Student
(Supervisor:Prof.Dr.Selim Aksoy)
Computer Engineering Department
Bilkent University
Abstract: Whole Slide Image (WSI) retrieval is inherently multi-label, yet existing methods fail to account for both the co-occurrence of pathological patterns and their varying clinical severity, resulting in retrieval that does not prioritize diagnostically significant matches. We propose an attention-based late interaction framework that operates on region-level representations extracted from pretrained foundation models, enabling fine-grained similarity estimation between WSIs. The model explicitly computes interactions between region embeddings and learns to aggregate them via class-specific attention, effectively assigning higher importance to diagnostically relevant correspondences. This yields a learned similarity function that prioritizes informative region pairs over uniform or fixed aggregation schemes. We further introduce a severity-aware multi-label similarity function that embeds clinical relevance into the retrieval objective, replacing binary similarity with a continuous measure of multi-label similarity, and use it to supervise retrieval via a margin-based ranking objective. Experiments on the BRACS dataset demonstrate consistent improvements over state-of-the-art methods, while providing interpretable attention-based evidence.
DATE: April 06, Monday @ 16:30 Place: EA 502