3 papers
cs.CV2026
RaLMPH: Reliability-aware Learning for Multi-Pathologist Harmonization in Whole-Slide Image Classification
Sungrae Hong, Jiwon Jeong, Soeun Cheon +5
Multiple Instance Learning (MIL) is a standard paradigm for Whole-Slide Image (WSI) analysis and has achieved strong results in computational pathology. However, most MIL pipelines…
cs.CV2026
Every Error has Its Magnitude: Asymmetric Mistake Severity Training for Multiclass Multiple Instance Learning
Sungrae Hong, Jiwon Jeong, Jisu Shin +4
Multiple Instance Learning (MIL) has emerged as a promising paradigm for Whole Slide Image (WSI) diagnosis, offering effective learning with limited annotations. However, existing…
cs.CV2025
VLEER: Vision and Language Embeddings for Explainable Whole Slide Image Representation
Anh Tien Nguyen, Keunho Byeon, Kyungeun Kim +1
Recent advances in vision-language models (VLMs) have shown remarkable potential in bridging visual and textual modalities. In computational pathology, domain-specific VLMs, which…