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This is the implementation of the MVCM model mentioned in our paper 'Validation of artificial intelligence contrast mammography in diagnosis of breast cancer: Relationship to histopathological results'.
Our new mammography database, LAMISDMDB, can give a breakthrough in detecting and classifying breast cancer. It is ready to use ML and DL algorithms to detect and classify different cancers within the breasts accurately. This database has a large size as compared to other public mammogram databases.
Independent evaluation of a multi-view multi-task convolutional neural network breast cancer classification model using Finnish mammography screening data
A mammographic mass detection and segmentation approach using a multi-scale morphological sifting approach integrated with a mean shift filter, k-means, and post-processing that detects and segments breast masses. This approach was on the InBreast mammographic dataset for Image Analysis course in MAIA Master's degree.
Mammo Lingua is a GUI application for Name Entity Recognition (NER) and BIRADS Classification. The application is built using Python with PyQt5 for the GUI and SpaCy for NER. The goal is to provide a tool that can analyze medical texts, identify named entities, and classify BIRADS categories.
This repository contains the training and testing codes for the paper "Imposing noise correlation fidelity on digital breast tomosynthesis restoration through deep learning techniques", submitted to the IWBI 2022 conference.