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

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

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

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

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

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

Noise Robust Learning with Hard Example Aware for Pathological Image classification

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

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

Colorectal Disease Classification Using ResNet and ResNeXt

Combining epigenetic modeling with machine learning for colorectal cancer detection

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

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

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

Colorectal cancer risk mapping through Bayesian Networks

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

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