2 citations · 3 across the 5 of their papers we have counts for
9 papers
Missing Modality-Aware Calibration for Trustworthy Brain Tumor Segmentation
Sol Lee, Hyunji Kim, Sungrae Hong +2
Multimodal brain tumor segmentation typically leverages multiple MRI modalities, yet incomplete modality acquisition is common in clinical practice due to protocol heterogeneity an…
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…
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…
Diagnose Like A REAL Pathologist: An Uncertainty-Focused Approach for Trustworthy Multi-Resolution Multiple Instance Learning
Sungrae Hong, Sol Lee, Jisu Shin +2
With the increasing demand for histopathological specimen examination and diagnostic reporting, Multiple Instance Learning (MIL) has received heightened research focus as a viable…
Priority-Aware Clinical Pathology Hierarchy Training for Multiple Instance Learning
Sungrae Hong, Kyungeun Kim, Juhyeon Kim +4
Multiple Instance Learning (MIL) is increasingly being used as a support tool within clinical settings for pathological diagnosis decisions, achieving high performance and removing…
CP-Dilatation: A Copy-and-Paste Augmentation Method for Preserving the Boundary Context Information of Histopathology Images
Sungrae Hong, Sol Lee, Mun Yong Yi
Medical AI diagnosis including histopathology segmentation has derived benefits from the recent development of deep learning technology. However, deep learning itself requires a la…
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…
MicroMIL: Graph-Based Multiple Instance Learning for Context-Aware Diagnosis with Microscopic Images
Jongwoo Kim, Bryan Wong, Huazhu Fu +3
Cancer diagnosis has greatly benefited from the integration of whole-slide images (WSIs) with multiple instance learning (MIL), enabling high-resolution analysis of tissue morpholo…