A deep learning approach to predicting breast tumor proliferation scores for the TUPAC16 challenge
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Updated
Mar 7, 2019 - Jupyter Notebook
A deep learning approach to predicting breast tumor proliferation scores for the TUPAC16 challenge
This repository contains morden baysian statistics and deep learning based research articles , software for survival analysis
AI-based pathology predicts origins for cancers of unknown primary - Nature
Chaste - Cancer Heart And Soft Tissue Environment - main public repository.
PhysiCell: Scientist end users should use latest release! Developers please fork the development branch and submit PRs to the dev branch. Thanks!
An R package for studying mutational signatures and structural variant signatures along clonal evolution in cancer.
Tool to visualize gigantic pathology images and use AI to segment cancer cells and present as an overlay
Cancer Predisposition Sequencing Reporter (CPSR)
A unified downloader+preprocessor for cancer genomics datasets
Some accessible radiomics datas were provided in this link.
cfDNAPro specializes in standardized and robust cfDNA fragmentomic analysis
Data analysis scripts for Rendeiro et. al, 2016 (doi:10.1038/ncomms11938)
A PyTorch implementation for differentiating between different types of lymphoma cancer
Anticancer Peptide Identification employing Multi-headed Deep-CNN
Android app testing reaction times during awake brain surgeries
Image classification on lung and colon cancer histopathological images through Capsule Networks or CapsNets.
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