Homomorphic Encryption and Federated Learning based Privacy-Preserving
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Updated
Jun 27, 2023 - Makefile
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
Official Pytorch implementation of MICCAI 2024 paper (early accept, top 11%) Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in Mammography
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.
Breast Cancer classification of INbreast dataset Mammograms with VGG-16 and SVM in hybrid setting using transfer learning.
This Repository Contains different Machine Learning Projects on various dataset. From Exploratory Data Analysis - Visualization to Prediction and Classification..
Multiple Disease Prediction System
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.
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.
SWSSL - Sliding window-based self-supervised learning for anomaly detection in high-resolution images (IEEE Trans. on Medical Imaging 2023)
A text-based computational framework for patient -specific modeling for classification of cancers. iScience (2022).
Streamlit application to classify cancer as malignant or benign.
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.
HRadNet: A Hierarchical Radiomics-based Network for Multicenter Breast Cancer Molecular Subtypes Prediction, TMI
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