6 papers
Beyond Relevance: Bayesian Evidence Acquisition for Agentic Whole-Slide Image Reasoning
Bryan Wong, Xun Xu, Huazhu Fu +2
Whole-slide image (WSI) reasoning requires an agent to sequentially acquire visual evidence before answering a diagnostic question. Existing training-free agentic frameworks formul…
Leveraging Spatial Context for Positive Pair Sampling in Histopathology Image Representation Learning
Willmer Rafell Quinones Robles, Sakonporn Noree, Jongwoo Kim +3
Deep learning has shown strong potential in cancer classification from whole-slide images (WSIs), but the need for extensive expert annotations often limits its success. Annotation…
Few-Shot Learning from Gigapixel Images via Hierarchical Vision-Language Alignment and Modeling
Bryan Wong, Jong Woo Kim, Huazhu Fu +1
Vision-language models (VLMs) have recently been integrated into multiple instance learning (MIL) frameworks to address the challenge of few-shot, weakly supervised classification…
PreMix: Label-Efficient Multiple Instance Learning via Non-Contrastive Pre-training and Feature Mixing
Bryan Wong, Mun Yong Yi
Multiple instance learning (MIL) has emerged as a powerful framework for weakly supervised whole slide image (WSI) classification, enabling slide-level predictions without requirin…
Towards Classifying Histopathological Microscope Images as Time Series Data
Sungrae Hong, Hyeongmin Park, Youngsin Ko +3
As the frontline data for cancer diagnosis, microscopic pathology images are fundamental for providing patients with rapid and accurate treatment. However, despite their practical…
Rethinking Pre-Trained Feature Extractor Selection in Multiple Instance Learning for Whole Slide Image Classification
Bryan Wong, Sungrae Hong, Mun Yong Yi
Multiple instance learning (MIL) has become a preferred method for gigapixel whole slide image (WSI) classification without requiring patch-level annotations. Current MIL research…