Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification. ICIAR 2018 Grand Challenge on BreAst Cancer Histology images (BACH)

Breast density classification with deep convolutional neural networks

An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization

High-resolution breast cancer screening with multi-view deep convolutional neural networks

Awesome artificial intelligence in cancer diagnostics and oncology

A Machine Learning and Deep Learning based webapp used to predict multiple diseases.

This repository was used to develop Mirai, the risk model described in: Towards Robust Mammography-Based Models for Breast Cancer Risk.

Machine learning classifier for cancer tissues 🔬

1st place of Kaggle's RSNA Screening Mammography Breast Cancer Detection competition

Meta-repository of screening mammography classifiers

Breast Cancer Detection classifier built from the The Breast Cancer Histopathological Image Classification (BreakHis) dataset composed of 7,909 microscopic images.

This CNN is capable of diagnosing breast cancer from an eosin stained image. This model was trained using 400 images. It has an accuracy of 80%

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides, BCNB Dataset

Code for Paper: Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification

Microwave Radar-based Imaging Toolbox (MERIT) is free and open-source software for microwave radar-basaed imaging. Including getting started guides and example data, MERIT is a flexible and extensible framework for developing, testing, running and optimising radar-based imaging algorithms.

Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis

A menu based multiple chronic disease detection system which will detect if a person is suffering from a severe disease by taking an essential input image.

This project aims to predict people who will survive breast cancer using machine learning models with the help of clinical data and gene expression profiles of the patients.

👀 Tobii Eye Tracker 4C Setup