20 citations · 42 across the 13 of their papers we have counts for
5 papers · 1 filter
TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model
Meilong Xu, Saumya Gupta, Xiaoling Hu +5
Accurately modeling multi-class cell topology is crucial in digital pathology, as it provides critical insights into tissue structure and pathology. The synthetic generation of cel…
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…
Topology-Guided Multi-Class Cell Context Generation for Digital Pathology
Shahira Abousamra, Rajarsi Gupta, Tahsin Kurc +3
In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis. To model such complex cell context, however, is challenging…
Computational Pathology: A Survey Review and The Way Forward
Mahdi S. Hosseini, Babak Ehteshami Bejnordi, Vincent Quoc-Huy Trinh +18
Computational Pathology CPath is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main ob…
Self Pre-training with Masked Autoencoders for Medical Image Classification and Segmentation
Lei Zhou, Huidong Liu, Joseph Bae +3
Masked Autoencoder (MAE) has recently been shown to be effective in pre-training Vision Transformers (ViT) for natural image analysis. By reconstructing full images from partially…