2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CV2023
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning
Saarthak Kapse, Srijan Das, Jingwei Zhang +4
We propose DiRL, a Diversity-inducing Representation Learning technique for histopathology imaging. Self-supervised learning techniques, such as contrastive and non-contrastive app…
eess.IV2023★ 2 cited
SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology
Jingwei Zhang, Ke Ma, Saarthak Kapse +4
Semantic segmentations of pathological entities have crucial clinical value in computational pathology workflows. Foundation models, such as the Segment Anything Model (SAM), have…
cs.CV2023
ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis
Xuan Xu, Saarthak Kapse, Rajarsi Gupta +1
Generative AI has received substantial attention in recent years due to its ability to synthesize data that closely resembles the original data source. While Generative Adversarial…