2 citations · 2 across the 10 of their papers we have counts for
10 papers
COAST: Context-Aware Differential Learning for Gene Expression Prediction in Spatial Transcriptomics
Keunho Byeon, Sunhong Park, Jeewoo Lim +1
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…
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 thi…
Hierarchical Classification for Improved Histopathology Image Analysis
Keunho Byeon, Jinsol Song, Seong Min Hong +2
Whole-slide image analysis is essential for diagnostic tasks in pathology, yet existing deep learning methods primarily rely on flat classification, ignoring hierarchical relations…
Pathology-Informed Latent Diffusion Model for Anomaly Detection in Lymph Node Metastasis
Jiamu Wang, Keunho Byeon, Jinsol Song +4
Anomaly detection is an emerging approach in digital pathology for its ability to efficiently and effectively utilize data for disease diagnosis. While supervised learning approach…
Normal and Abnormal Pathology Knowledge-Augmented Vision-Language Model for Anomaly Detection in Pathology Images
Jinsol Song, Jiamu Wang, Anh Tien Nguyen +4
Anomaly detection in computational pathology aims to identify rare and scarce anomalies where disease-related data are often limited or missing. Existing anomaly detection methods,…
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…
Benchmarking Pathology Foundation Models: Adaptation Strategies and Scenarios
Jeaung Lee, Jeewoo Lim, Keunho Byeon +1
In computational pathology, several foundation models have recently emerged and demonstrated enhanced learning capability for analyzing pathology images. However, adapting these mo…
CAMP: Continuous and Adaptive Learning Model in Pathology
Anh Tien Nguyen, Keunho Byeon, Kyungeun Kim +3
There exist numerous diagnostic tasks in pathology. Conventional computational pathology formulates and tackles them as independent and individual image classification problems, th…
DIOR-ViT: Differential Ordinal Learning Vision Transformer for Cancer Classification in Pathology Images
Ju Cheon Lee, Keunho Byeon, Boram Song +2
In computational pathology, cancer grading has been mainly studied as a categorical classification problem, which does not utilize the ordering nature of cancer grades such as the…
Centroid-aware feature recalibration for cancer grading in pathology images
Jaeung Lee, Keunho Byeon, Jin Tae Kwak
Cancer grading is an essential task in pathology. The recent developments of artificial neural networks in computational pathology have shown that these methods hold great potentia…