Federated Learning for Computational Pathology - Medical Image Analysis
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Updated
Feb 9, 2022 - Python
Federated Learning for Computational Pathology - Medical Image Analysis
HistoSeg is an Encoder-Decoder DCNN which utilizes the novel Quick Attention Modules and Multi Loss function to generate segmentation masks from histopathological images with greater accuracy. This repo contains the code to Test and Train the HistoSeg
Python package that uses colorspace-based segmentation to analyze histopathology images.
CODAvision - open source medical image labeling tool
Pytorch implementation of NEGEV method. Paper: "Negative Evidence Matters in Interpretable Histology Image Classification".
Constrained self-supervised method with temporal ensembling for fiber bundle detection on anatomic tracing data
Deep-learning based classification pipeline for subtyping lung tumors from histology. Study design and codebase to analyze the impact of nucleus segmentation on subtyping.
DAN-NucNet: A dual attention based framework for nuclei segmentation in cancer histology images under wild clinical conditions
CUCA: Predicting fine-grained cell types from histology images through cross-modal learning in spatial transcriptomics
An automated image analysis tool for quantification of fat cells
SectraPlugin Histolung — Python ML pipeline for lung histology WSI analysis (Docker & PyTorch) | HES-SO student bachelor thesis project 2024
[ISBI 2024] Accurate Subtyping of Lung Cancers by Modelling Class Dependencies
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