breast-cancer

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

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

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

Awesome artificial intelligence in cancer diagnostics and oncology

Machine learning classifier for cancer tissues 🔬

Meta-repository of screening mammography classifiers

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.

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.

Differences between human and machine perception in medical diagnosis

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

Algorithm to segment pectoral muscles in breast mammograms

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.

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

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

👀 Tobii Eye Tracker 4C Setup

OncoText is an information extraction service for breast pathology reports. It supports over 20 categories including DCIS, includes pretrained models, and supports flexible addition of new categories, new training data, and parsing new reports.

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

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.

Classifying malignant and benign tumors using Neural Networks 🔬

Ai powered web app to detect Metastatic Cancer and Invasive Ductal Carcinoma in histopathology tissue images.

An implementation of the L2-SVM for breast cancer detection using the Wisconsin diagnostic dataset.

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.

SigMa is a probabilistic model for the sequential dependencies of mutation signatures

The Invasive Ductal Carcinoma (IDC) Detection System is an open source computer vision program created to classify IDC positive and negative samples. This project is a complete system including a locally hosted webserver / UI / API allowing you to manage your pipeline.

Flask based Web app with 5 Machine Learning Models including 10 most common Disease prediction and Coronavirus prediction with their symptoms as inputs and Breast cancer , Chronic Kidney Disease and Heart Disease predictions with their Medical report as inputs

📎 About MIMBCD-UI Project