Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

Deep Learning to Improve Breast Cancer Detection on Screening Mammography

Classification of Breast Cancer diagnosis Using Support Vector Machines

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

BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix

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

A Flask based web application to predict breast cancer.

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

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

Data processing and analysis related code associated with the study "A single-cell and spatially resolved atlas of human breast cancers".

Breast Cancer Detection Using Machine Learning

Master's dissertation for breast cancer detection in mammograms using deep learning techniques in Tensorflow. Contains the final report and source code.

Multiple disease prediction such as Diabetes, Heart disease, Kidney disease, Breast cancer, Liver disease, Malaria, and Pneumonia using supervised machine learning and deep learning algorithms.

Her2 Breast Cancer Project

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

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

Analysis workflow for MRC-IEU PhD mini-project on childhood BMI effect on breast cancer

Official code for Breast Cancer Histopathology Image Classification and Localization using Multiple Instance Learning

Machine learning classifier for cancer tissues 🔬

Using Convolutional Neural Networks (CNN) for Semantic Segmentation of Breast Cancer Lesions (BRCA). Master's thesis

ResearchKit app studying Breast Cancer, developed by Sage Bionetworks.

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

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

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%

[ICMLSC 2018] On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset

AI app to detect breast cancer in histopathological database

乳腺癌检测分类数据

Experiments to show the usage of deep learning to detect breast cancer from breast histopathology images

Youtube Tutorial: Streamlit app that predicts Breast Cancer

Machine Learning Patient Risk Analyzer Solution Accelerator is an end-to-end (E2E) healthcare app that leverages ML prediction models (e.g., Diabetes Mellitus (DM) patient 30-day re-admission, breast cancer risk, etc.) to demonstrate how these models can provide key insights for both physicians and patients. Patients can easily access their appointment and care history with infused cognitive services through a conversational interface. In addition to providing new insights for both doctors and patients, the app also provides the Data Scientist/IT Specialist with one-click experiences for registering and deploying a new or existing model to Azure Kubernetes Clusters, and best practices for maintaining these models through Azure MLOps.