GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection (https://osf.io/84e7f/)

A multi-centre polyp detection and segmentation dataset for generalisability assessment https://www.nature.com/articles/s41597-023-01981-y

Noise Robust Learning with Hard Example Aware for Pathological Image classification

Official Implementation of our paper "Supervision meets Self-Supervision: A Deep Multitask Network for Colorectal Cancer Histopathological Analysis" [Best Paper Award at MISP 2022]

DL-model for multi-class tissue segmentation in colorectal cancer H&E slides, developed as part of the SemiCOL2023 Challenge.

Colorectal cancer risk mapping through Bayesian Networks

Decision model for colorrectal cancer screening. Based on bayesian networks and influence diagrams

This repository contains all machine learning and statistical models used to analyze the landscape of colorectal cancer.

Transfer learning & fine-tuning in Tensorflow for classification of textures in colorectal cancer histology

UNSUPERVISED MACHINE LEARNING (CLUSTERING): TCGA data mining for studying the system of interactions between sub-branches of Wnt signalling pathway in colorectal cancer

Diagnosing colorectal cancer from histopathology images using deep learning: final project code.

This project predicts the five-year survival rates of colorectal cancer patients using a Random Forest machine learning model.

Based on our paper "SnapEnsemFS: A Snapshot Ensembling-based Deep Feature Selection Model for Colorectal Cancer Histological Analysis" published in Scientific Reports, Nature (2023).

The goal of this analysis is to explore the machine learning-based automatic diagnosis of colorectal patients based on the single nucleotide polymorphisms (SNP). Such a computational approach may be used complementary to other diagnosis tools, such as, biopsy, CT scan, and MRI. Moreover, it may be used as a low-cost screening for colorectal cancers

R scripts and data for clustering-based molecular subtyping and mutational signature analysis in colorectal cancer (CRC), including visualization and survival analysis tools.

Colorectal Disease Classification Using ResNet and ResNeXt

Colorectal cancer detection from gut microbiome DNA sequencing using machine learning.