Reinforcement learning reading list (including medical image analysis papers )
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Updated
Jul 9, 2018
Reinforcement learning reading list (including medical image analysis papers )
Reading list for Computational Pathology
HistoSegNet: Semantic Segmentation of Histological Tissue Type in Whole Slide Images (ICCV 2019)
Elm library providing encoder/decoder for automated slide analysis platform XML format (https://github.com/computationalpathologygroup/ASAP).
Atlas of Digital Pathology for Deep Learning [CVPR2019]
Rule-Based Thyroid Whole Slide Image Diagnosis
Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation
Tools for tissue image stain normalisation and augmentation in Python 3
Whole Slide Image segmentation with weakly supervised multiple instance learning on TCGA | MICCAI2020 https://arxiv.org/abs/2004.05024
Probeable DARTS with Application to Computational Pathology [ICCV2021]
Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology (LMRL Workshop, NeurIPS 2021)
Python 3 library for the augmentation & normalization of H&E images
Suspicious Regions-Based Whole Slide Image Analysis
🔬 Syntax - the arrangement of whole-slide-images and their image tiles to create well-formed computational pathology pipelines.
Contains code for Semantic Segmentation of MoNuSeg 2018 challenge.
Contains all research papers read since the end of July 2020 👍
Graph neural networks for PDAC vs CP in histology
Code for our BVM workshop submission "Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays"
Cytomine-Core is the main web server implementing the Cytomine API
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