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

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.

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

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 🔬

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

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%

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.

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

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 is a Machine Learning web app developed using Python and StreamLit. Uses algorithms like Logistic Regression, KNN, SVM, Random Forest, Gradient Boosting, and XGBoost to build powerful and accurate models to predict the status of the user (High Risk / Low Risk) with respect to Heart Attack and Breast Cancer.