6 papers
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…
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…