GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection https://drive.google.com/drive/folders/1T35gqO7jIKNxC-gVA2YVOMdsL7PSqeAa?usp=sharing

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

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

Noise Robust Learning with Hard Example Aware for Pathological Image classification

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

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

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

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

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

Prediction of colorectal cancer (CRC) phenotype based on Microbiome Metagenomics

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

Combining epigenetic modeling with machine learning for colorectal cancer detection

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

Colorectal Disease Classification Using ResNet and ResNeXt

Determines if a given Colorectal tissue image is cancerous or healthy using methods from Topology for the input embedding (TDA).