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

Homomorphic Encryption and Federated Learning based Privacy-Preserving

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.

Multiple Disease Prediction System

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

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

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

Breast cancer detection using machine learning with deployment of model

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

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

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

Streamlit application to classify cancer as malignant or benign.

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

Deep Learning in Medicine Final Project

A novel deep learning based technique for effective cancer detection.

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.

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