[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

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

🩺 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.

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

Breast Cancer Image Classification On WSI With Spatial Correlations https://teacher.bupt.edu.cn/zhuchuang/en/index.htm

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

https://teacher.bupt.edu.cn/zhuchuang/en/index.htm

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)

Streamlit application to classify cancer as malignant or benign.

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