This repo has been archived, these workflows are still available in the GATK repository under the scripts directory. The workflows are also organized in Dockstore in the GATK Best Practices Workflows collection.
DeTiN is designed to measure tumor-in-normal contamination and improve somatic variant detection sensitivity when using a contaminated matched control.
Deep Learning for Automatic Differential Diagnosis of Primary Central Nervous System Lymphoma and Glioblastoma: Multi-parametric MRI based Convolutional Neural Network Model
Code, data and model for Pérez-García et al. 2020, "A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections"
The work presented explains how to segment the brain tumour area in absence of interaction with user basing his technique on a saliency map constructed from three different resonance techniques.
This repository covers a brain scan tumor classification project for the University of Washington DATA 515 course. In our project we train a CNN to predict if a MRI scan (.jpg, .png, .jpeg) is tumorous or not.
