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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.IR2026
Every Preference Has Its Strength: Injecting Ordinal Semantics into LLM-Based Recommenders
Jiwon Jeong, Donghee Han, Sungrae Hong +2
Recent work has shown that large language models (LLMs) can enhance recommender systems by integrating collaborative filtering (CF) signals through hybrid prompting. However, most…
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…