2 citations · 4 across the 12 of their papers we have counts for
13 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…
PathReportEval: A Systematic Benchmark for Pathology Report Generation
Suryakant Singh, Sejuti Majumder, Beatrice Knudsen +2
Pathology report generation from whole-slide images (WSIs) is a rapidly growing multimodal learning problem, yet progress is difficult to measure because existing studies use heter…
Semantic Context-aware mOdality fUsion Transformer (SCOUT): A Context-Aware Multimodal Transformer for Concept-Grounded Pathology Report Generation
Suryakant Singh, Saarthak Kapse, Joel Saltz +1
Whole-slide pathology report generation requires models to integrate localized histomorphology, global tissue context, and diagnostically relevant semantic information, yet existin…
TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning
Varun Belagali, Saarthak Kapse, Pierre Marza +12
The interpretation of small tiles in large whole slide images (WSI) often needs a larger image context. We introduce TICON, a transformer-based tile representation contextualizer t…
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…
CDG-MAE: Cross-view Masked Modeling using Diffusion Generated Views
Varun Belagali, Pierre Marza, Srikar Yellapragada +7
Cross-view masked autoencoding has emerged as a powerful pretext task for learning dense correspondences, which are essential for applications such as video label propagation. The…
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…
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…
Pathology Image Compression with Pre-trained Autoencoders
Srikar Yellapragada, Alexandros Graikos, Kostas Triaridis +6
The growing volume of high-resolution Whole Slide Images in digital histopathology poses significant storage, transmission, and computational efficiency challenges. Standard compre…
Gen-SIS: Generative Self-augmentation Improves Self-supervised Learning
Varun Belagali, Srikar Yellapragada, Alexandros Graikos +7
Self-supervised learning (SSL) methods have emerged as strong visual representation learners by training an image encoder to maximize similarity between features of different views…
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…
RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency
Wentao Huang, Meilong Xu, Xiaoling Hu +9
Spatial transcriptomics (ST) provides essential spatial context by mapping gene expression within tissue, enabling detailed study of cellular heterogeneity and tissue organization.…
Semi-Supervised Contrastive VAE for Disentanglement of Digital Pathology Images
Mahmudul Hasan, Xiaoling Hu, Shahira Abousamra +3
Despite the strong prediction power of deep learning models, their interpretability remains an important concern. Disentanglement models increase interpretability by decomposing th…