Deep Learning to Improve Breast Cancer Detection on Screening Mammography

BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix

Breast Cancer Detection Using Machine Learning

Master's dissertation for breast cancer detection in mammograms using deep learning techniques in Tensorflow. Contains the final report and source code.

1st place solution of RSNA Screening Mammography Breast Cancer Detection competition on Kaggle: https://www.kaggle.com/competitions/rsna-breast-cancer-detection

[MICCAI 2024, top 11%] Official Pytorch implementation of Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in Mammography

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

[ICMLSC 2018] On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset

AI app to detect breast cancer in histopathological database

Experiments to show the usage of deep learning to detect breast cancer from breast histopathology images

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

This project uses mammograms for breast cancer detection using deep learning techniques.

Breast Cancer Detection using ML

This project is made in Matlab Platform and it detects whether a person has cancer or not by taking into account his/her mammogram.

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Using pre-trained model to classify images to detect cancerous cells

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Breast cancer has the second highest mortality rate in women next to lung cancer. As per clinical statistics, 1 in every 8 women is diagnosed with breast cancer in their lifetime. However, periodic clinical check-ups and self-tests help in early detection and thereby significantly increase the chances of survival. Invasive detection techniques cause rupture of the tumor, accelerating the spread of cancer to adjoining areas. Hence, there arises the need for a more robust, fast, accurate, and efficient non-invasive cancer detection system. Early detection can give patients more treatment options. In order to detect signs of cancer, breast tissue from biopsies is stained to enhance the nuclei and cytoplasm for microscopic examination. Then, pathologists evaluate the extent of any abnormal structural variation to determine whether there are tumors. Architectural Distortion (AD) is a very subtle contraction of the breast tissue and may represent the earliest sign of cancer. Since it is very likely to be unnoticed by radiologists, several approaches have been proposed over the years but none using deep learning techniques. AI will become a transformational force in healthcare and soon, computer vision models will be able to get a higher accuracy when researchers have the access to more medical imaging datasets. The application of machine learning models for prediction and prognosis of disease development has become an irrevocable part of cancer studies aimed at improving the subsequent therapy and management of patients. The application of machine learning models for accurate prediction of survival time in breast cancer on the basis of clinical data is the main objective. We have developed a computer vision model to detect breast cancer in histopathological images. Two classes will be used in this project: Benign and Malignant

Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN

SVM (Support Vector Machines) is used to build and train a model using human cell records, and classify cells to predict whether the samples are benign or malignant and display output using Flask Application On Heroku

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A web app for breast cancer detection.

Implementation of SVM Classifier To Perform Classification on the dataset of Breast Cancer Wisconin; to predict if the tumor is cancer or not.

Matlab based GUI to predict breast cancer using Deep Learning

AI Breast cancer detection using InBreast, CBIS-DDSM, MIAS mammography image datasets

An implementation of the L2-SVM for breast cancer detection using the Wisconsin diagnostic dataset.

This is a guided certification project, as a part of Data Science for Social Good initiative

End-to-end breast cancer detection in Python

Detect Breast Cancer using ANN and Random Forest