Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering
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Updated
Apr 29, 2024 - Python
Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering
Cancer metastasis detection with neural conditional random field (NCRF)
Read and write TIFF files
Computational Pathology Toolbox developed by TIA Centre, University of Warwick.
DSMIL: Dual-stream multiple instance learning networks for tumor detection in Whole Slide Image
Deep learning library for digital pathology, with both Tensorflow and PyTorch support.
Deep Learning Inferred Multiplex ImmunoFluorescence for IHC Image Quantification (https://deepliif.org) [Nature Machine Intelligence'22, CVPR'22, MICCAI'23, Histopathology'23, MICCAI'24]
Whole Slide Image segmentation with weakly supervised multiple instance learning on TCGA | MICCAI2020 https://arxiv.org/abs/2004.05024
Code for the paper " PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers "
A package for working with whole-slide data including a fast batch iterator that can be used to train deep learning models.
Implementation of Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification approach.
🔬 Syntax - the arrangement of whole-slide-images and their image tiles to create well-formed computational pathology pipelines.
🔥 🚀 Blazingly fast pipeline for patch-based classification in whole slide images
Digital Pathology Whole Slide Image Analysis Toolbox
Simple library for preprocessing histopathological whole-slide images (WSI) into tiles (a.k.a. patches) towards deep learning
Whole Slide Digital Pathology Image Tissue Localization
Python library for processing whole slide images (WSIs) in sdpc format
Deep learning enabled assessment of cardiac allograft rejection from endomyocardial biopsies- Nature Medicine
This repo provides an exhaustive pipeline of processing TCGA whole-slide images for downstream multiple instance learning.
Bayesian Inference of Slide-level Confidence via Uncertainty Index Thresholding
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