[MICCAI 2024, top 11%] Official Pytorch implementation of Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in Mammography

Homomorphic Encryption and Federated Learning based Privacy-Preserving

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

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

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.

Algorithm to segment pectoral muscles in breast mammograms

🩺 Advanced neural network for breast cancer classification using Wisconsin dataset. Analyzes cell nucleus characteristics from FNA samples to distinguish malignant/benign masses with 96.5% accuracy. Features comprehensive documentation, automated setup, testing framework, and deployment guides. Educational ML project with 15,000+ lines of docs.

🏥 AI-powered breast cancer classification using Logistic Regression with 95% accuracy. Features interactive Gradio web interface for real-time predictions on 30 diagnostic parameters from Wisconsin dataset. Includes comprehensive Jupyter notebooks for model training, evaluation metrics, and deployment-ready architecture for healthcare application.

1st place solution to the Breast Cancer Classification Task of HeLP Challenge 2019.

This Repository Contains different Machine Learning Projects on various dataset. From Exploratory Data Analysis - Visualization to Prediction and Classification..

Memory-aware curriculum federated learning for breast cancer classification. Computer Methods and Programs in Biomedicine.

Multiple Disease Prediction System

Breast cancer detection using machine learning with deployment of model

In this repository, I implemented the deep learning classifier introduced in the paper "Deep Learning to Improve Breast Cancer Detection on Screening Mammography" using PyTorch.

Using the Knn algorithm, it detects whether the tumor is benign or malignant in people diagnosed with breast cancer.

SWSSL - Sliding window-based self-supervised learning for anomaly detection in high-resolution images (IEEE Trans. on Medical Imaging 2023)

HRadNet: A Hierarchical Radiomics-based Network for Multicenter Breast Cancer Molecular Subtypes Prediction, TMI

Streamlit application to classify cancer as malignant or benign.

A text-based computational framework for patient -specific modeling for classification of cancers. iScience (2022).

Make predictions for breast cancer, malignant or benign using the Breast Cancer data set

Breast cancer detection using machine learning classification is a project where you build a model to identify whether a given set of medical features indicates the presence of breast cancer. This project involves using a labeled dataset of medical records, where each record is classified as either indicating breast cancer or not.

Analysing and predicting wheter the cancer is benign or malignant using machine learning models.

Official repository of Team BCN-AIM to MICCAI 2025 ODELIA-Challenge.

Code for classifying breast cancer tumors using machine learning. Includes preprocessing, visualizations, and models like Logistic Regression, Decision Tree, and Random Forest. Evaluated with accuracy, precision, recall, and F1-score. Clone, install dependencies, and run the Jupyter notebook for full analysis.

Breast cancer image classification on the BreaKHis dataset - The purpose of this project was to experiment with different methods for accurately detecting breast cancer types (benign, malign) and then all their subtypes (e.g. Carcinoma). The F1-scores are 99% with the binary and 94% with the multi-class model.

A novel deep learning based technique for effective cancer detection.

No description provided.

Harnessing Deep Learning for Enhanced Mammogram Analysis

logistic regression from scratch using python to solve binary classification problem using breast cancer dataset from scikit-learn. A complete breakdown of logistic regression algorithm.

Breast cancer classification and evaluation of classifiers