Official code for Breast Cancer Histopathology Image Classification and Localization using Multiple Instance Learning

Machine learning classifier for cancer tissues 🔬

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

Breast Cancer Detection classifier built from the The Breast Cancer Histopathological Image Classification (BreakHis) dataset composed of 7,909 microscopic images.

This CNN is capable of diagnosing breast cancer from an eosin stained image. This model was trained using 400 images. It has an accuracy of 80%

Machine learning is widely used in bioinformatics and particularly in breast cancer diagnosis. In this project, certain classification methods such as K-nearest neighbors (K-NN) and Support Vector Machine (SVM) which is a supervised learning method to detect breast cancer are used.

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

Pluralistic Image Completion for Anomaly Detection (Med. Image Anal. 2023)

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

Python feed-forward neural network to predict breast cancer. Trained using stochastic gradient descent in combination with backpropagation.

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

The aim of the project, to determine whether the breast cancer cell is malignant or benign.I got the dataset from Kaggle.

An experiment using neural networks to predict obesity-related breast cancer over a small dataset of blood samples.

Comparison of Different Machine Learning Classification Algorithms for Breast Cancer Prediction

The repository provides code for running inference with different breast cancer models, links for downloading the trained model checkpoints, and example notebooks on how work with a DICOM pipeline.

On-spot training to enhance the performance of traditional machine learning algorithms, applied to the prediction of breast cancer malignity from ultrasound images

Predictive Modelling of Pathological Complete Response Classification and Relapse-Free Survival Regression in Cancer Patients

This repository contains a comparison of different Naive Bayes classifiers (Bernoulli, Gaussian, and Multinomial) for predicting benign and malignant cancer cases. The project includes confusion matrices for each classifier to evaluate their performance.

Supervised Learning Algorithms