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)

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

Breast density classification with deep convolutional neural networks

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

Awesome artificial intelligence in cancer diagnostics and oncology

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

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

1st place solution of RSNA Screening Mammography Breast Cancer Detection competition on Kaggle: https://www.kaggle.com/competitions/rsna-breast-cancer-detection

The MAMA-MIA Dataset: A Multi-Center Breast Cancer DCE-MRI Public Dataset with Expert Segmentations

This is the official repository for our CVPR 2023 paper 'Task-Specific Fine-Tuning via Variational Information Bottleneck for Weakly-Supervised Pathology Whole Slide Image Classification'.

Machine learning classifier for cancer tissues 🔬

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

Meta-repository of screening mammography classifiers

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

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.

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%

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.

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

Medical Diagnosis A Machine Learning Based Web Application