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SAM: Segment anything model (SAM) for digital pathology: Assess zero-shot segmentation on whole slide imaging

by Yuankai Huo Posted on November 10, 2023November 13, 2023

This is the official implementation of Segment anything model (SAM) for digital pathology: Assess zero-shot […]

Recent Code and Models

  • cellNN: Unveiling Local CellArrangement Pattern with Graph-based Signatures to Perform Cell Spatial Analysis in Crohn’s Disease November 12, 2023
  • Random-walk: Alleviating tiling effect in high-resolution histological whole slide image synthesis November 12, 2023
  • PixN2N-HD: Image Synthesis for Multiplexed Immunofluorescence Imaging November 12, 2023
  • MemSeg: Topological-Preserving Membrane Skeleton Segmentation in Multiplex Immunofluorescence Imaging November 12, 2023
  • MxIF Q-score: Biology-Informed Quality Assurance for Multiplexed Immunofluorescence Imaging November 12, 2023
  • GCA-informatics: A cross-platform informatics system for the Gut Cell Atlas: integrating clinical, anatomical and histological data November 12, 2023
  • CoNIC: Nucleus subtype classification using inter-modality learning November 12, 2023
  • GDL: Exploring shared memory architectures for end-to-end gigapixel deep learning November 12, 2023
  • Tangram: Influence of cell-type ratio on spatially resolved single-cell transcriptomes using the Tangram algorithm: based on implementation on single-cell and MxIF data November 10, 2023
  • SAM: Segment anything model (SAM) for digital pathology: Assess zero-shot segmentation on whole slide imaging November 10, 2023
  • UAD: feasibility of universal anomaly detection without kKnowing the abnormality in medical images November 10, 2023
  • CSMIL: cross-scale multi-instance learning for pathological image diagnosis November 9, 2023
  • Omni-Seg: a scale-aware dynamic network for pathological image segmentation November 9, 2023

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