Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification. ICIAR 2018 Grand Challenge on BreAst Cancer Histology images (BACH)
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Updated
Oct 23, 2019 - Python
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
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
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
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%
Breast Cancer Detection classifier built from the The Breast Cancer Histopathological Image Classification (BreakHis) dataset composed of 7,909 microscopic images.
Code for Paper: Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification
👀 Tobii Eye Tracker 4C Setup
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.
3D-GMIC: an efficient deep neural network to find small objects in large 3D images
[CVAMD 2021] "End-to-End Learning of Fused Image and Non-Image Feature for Improved Breast Cancer Classification from MRI"
A Django App for predicting Heart disease, Diabetes and Breast Cancer developed using Random Forest Classifier and KNN.
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.
An implementation of the L2-SVM for breast cancer detection using the Wisconsin diagnostic dataset.
SigMa is a probabilistic model for the sequential dependencies of mutation signatures
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