6 papers
Pathologist Attention-Aligned Report Generation for Prostate Histopathology
Ruoyu Xue, Suryakant Singh, Souradeep Chakraborty +12
The allocation of visual attention by pathologists during cancer diagnosis is a highly selective process that critically shapes the information extracted from whole-slide images (W…
PixCell: A generative foundation model for digital histopathology images
Srikar Yellapragada, Alexandros Graikos, Zilinghan Li +11
The digitization of histology slides has revolutionized pathology, providing massive datasets for cancer diagnosis and research. Self-supervised and vision-language models have bee…
Towards interpretable prediction of recurrence risk in breast cancer using pathology foundation models
Jakub R. Kaczmarzyk, Sarah C. Van Alsten, Alyssa J. Cozzo +5
Transcriptomic assays such as the PAM50-based ROR-P score guide recurrence risk stratification in non-metastatic, ER-positive, HER2-negative breast cancer but are not universally a…
Measuring and Predicting Where and When Pathologists Focus their Visual Attention while Grading Whole Slide Images of Cancer
Souradeep Chakraborty, Ruoyu Xue, Rajarsi Gupta +11
The ability to predict the attention of expert pathologists could lead to decision support systems for better pathology training. We developed methods to predict the spatio-tempora…
GECKO: Gigapixel Vision-Concept Contrastive Pretraining in Histopathology
Saarthak Kapse, Pushpak Pati, Srikar Yellapragada +5
Pretraining a Multiple Instance Learning (MIL) aggregator enables the derivation of Whole Slide Image (WSI)-level embeddings from patch-level representations without supervision. W…
ZoomLDM: Latent Diffusion Model for multi-scale image generation
Srikar Yellapragada, Alexandros Graikos, Kostas Triaridis +4
Diffusion models have revolutionized image generation, yet several challenges restrict their application to large-image domains, such as digital pathology and satellite imagery. Gi…