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Found 4 papers, 1 papers with code
Beyond attention: deriving biologically interpretable insights from weakly-supervised multiple-instance learning models
Recent advances in attention-based multiple instance learning (MIL) have improved our insights into the tissue regions that models rely on to make predictions in digital pathology.
A Graph Based Neural Network Approach to Immune Profiling of Multiplexed Tissue Samples
Multiplexed immunofluorescence provides an unprecedented opportunity for studying specific cell-to-cell and cell microenvironment interactions.
Ink removal from histopathology whole slide images by combining classification, detection and image generation models
Removal of marker ink from these high-resolution whole slide images is non-trivial and complex problem as they contaminate different regions and in an inconsistent manner.
Improving Whole Slide Segmentation Through Visual Context - A Systematic Study
While challenging, the dense segmentation of histology images is a necessary first step to assess changes in tissue architecture and cellular morphology.
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