Jin Tae Kwak

Test-Time Instance Selection for Improved Whole Slide Image Analysis

Quoc Viet Hung Nguyen, Sunhong Park, Jin Tae Kwak

Whole Slide Image (WSI) analysis has been widely studied for cancer diagnosis. Conventionally, a gigapixel WSI is divided into small patches and processed by Multiple Instance Learning (MIL) models. However, existing MIL models typically process all patches, many of which contain redundant …

Overview of the Trauma THOMPSON Challenge at MICCAI 2025

Yupeng Zhuo, Eddie Zhang, Xiangchen Yu, Aditya Pachpande et autres

us, ca, kr (code pays fourni par la source)

Welcome new doctor: Continual learning with expert consultation and autoregressive inference for whole slide image analysis

Doanh Cao Bui, Jin Tae Kwak

Whole Slide Image (WSI) analysis, with its ability to reveal detailed tissue structures in magnified views, plays a crucial role in cancer diagnosis and prognosis. Due to their giga-sized nature, WSIs require substantial storage and computational resources for processing and training predictive …

kr, jp (code pays fourni par la source)

COAST: Context-Aware Differential Learning for Gene Expression Prediction in Spatial Transcriptomics

Keunho Byeon, Sunhong Park, Jeewoo Lim, Jin Tae Kwak

Spatial transcriptomics enables profiling of spatial gene expression but is limited by high cost and low throughput, motivating prediction from H&E histopathology images. Existing context-aware methods mainly supervise absolute expression, while relative expression relationships between spots are rarely used explicitly. We propose …

TaxoMIL: Taxonomy-Constrained Learning for Hierarchical Whole Slide Image Analysis

Chaeyeon Lee, Khang Nguyen Quoc, Jinsol Song, Yosep Chong et autres

Whole slide image (WSI) analysis is central to computational pathology, with multiple instance learning (MIL) emerging as the standard pipeline for slide-level diagnosis. However, conventional approaches formulate WSI diagnosis as a flat classification task over discrete labels, contradicting the inherently hierarchical, coarse-to-fine …

TaxoMIL: Taxonomy-Constrained Learning for Hierarchical Whole Slide Image Analysis

Chaeyeon Lee, Khang Nguyen Quoc, Jinsol Song, Yosep Chong et autres

Whole slide image (WSI) analysis is central to computational pathology, with multiple instance learning (MIL) emerging as the standard pipeline for slide-level diagnosis. However, conventional approaches formulate WSI diagnosis as a flat classification task over discrete labels, contradicting the inherently hierarchical, coarse-to-fine …

kr, jp (code pays fourni par la source)

HEXST: Hexagonal Shifted-Window Transformer for Spatial Transcriptomics Gene Expression Prediction

Keunho Byeon, Jin Tae Kwak

Spatial transcriptomics offers spatially resolved gene expression profiling within tissue sections, but its cost and limited throughput hinder large-scale deployment. To extend this capability to routine practice, recent computational methods aim to infer spatial gene expression directly from ubiquitous hematoxylin and eosin-stained …

HEXST: Hexagonal Shifted-Window Transformer for Spatial Transcriptomics Gene Expression Prediction

Keunho Byeon, Jin Tae Kwak

Spatial transcriptomics offers spatially resolved gene expression profiling within tissue sections, but its cost and limited throughput hinder large-scale deployment. To extend this capability to routine practice, recent computational methods aim to infer spatial gene expression directly from ubiquitous hematoxylin and eosin-stained …

Hierarchical Classification for Improved Histopathology Image Analysis

Keunho Byeon, Jinsol Song, Seong Min Hong, Yosep Chong et autres

Whole-slide image analysis is essential for diagnostic tasks in pathology, yet existing deep learning methods primarily rely on flat classification, ignoring hierarchical relationships among class labels. In this study, we propose HiClass, a hierarchical classification framework for improved histopathology image analysis, that …

Hierarchical Classification for Improved Histopathology Image Analysis

Keunho Byeon, Jinsol Song, Seong Min Hong, Yosep Chong et autres

Whole-slide image analysis is essential for diagnostic tasks in pathology, yet existing deep learning methods primarily rely on flat classification, ignoring hierarchical relationships among class labels. In this study, we propose HiClass, a hierarchical classification framework for improved histopathology image analysis, that …

kr (code pays fourni par la source)

Two-Stage Residual-Guided Diffusion Framework for High-Fidelity Pathology Image Restoration

Jeewoo Chelsea Lim, Chi-Ho Yu, Jin Tae Kwak

The digitization of whole slide images in computational pathology frequently introduces out-of-focus blur, a prevalent artifact that can compromise diagnostic accuracy. Although recent diffusion-based restoration methods have shown notable success, they often fail to reconstruct the fine-grained cellular and textural details critical …

kr (code pays fourni par la source)

CAMP: continuous and adaptive learning model in pathology

Anh Tien Nguyen, Keunho Byeon, Kyungeun Kim, Boram Song et autres

Conventional computational pathology treats diagnostic tasks as independent and individual image classification problems, leading to inefficiencies and high costs. To address this, we introduce CAMP (Continuous and Adaptive learning Model in Pathology), a unified and universal framework for pathology image classification. CAMP …

kr (code pays fourni par la source)