6 papers
Test-Time Instance Selection for Improved Whole Slide Image Analysis
Quoc Anh 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 Lear…
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…
ViDRiP-LLaVA: A Dataset and Benchmark for Diagnostic Reasoning from Pathology Videos
Trinh T. L. Vuong, Jin Tae Kwak
We present ViDRiP-LLaVA, the first large multimodal model (LMM) in computational pathology that integrates three distinct image scenarios, including single patch images, automatica…
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…