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

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

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

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

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.

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.

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

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.

The aim is to categorize and accurately identify the types and subtypes of breast cancer

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.

Interpretable breast cancer classification using convolutional neural networks on mammographic images

Small project to accurately predict nature of a tumour (benign/malignant) using the UCI Wisconsin breast cancer dataset (https://www.kaggle.com/uciml/breast-cancer-wisconsin-data)

Breast Cancer Classifier

This Streamlit application serves as a breast cancer diagnosis tool, leveraging machine learning to predict whether a breast mass is benign or malignant based on cytology lab measurements. Users can input data either manually through sliders or connect it directly to their lab for analysis. The application features a radar chart visualization