Using deep learning to discover interpretable representations for mammogram classification and explanation

DeepHealth Annotate is a web-based tool for viewing and annotating DICOM images. Annotation metadata can be exported in JSON format to be used for a variety of purposes, such as creating training input for deep learning models that use bounding box algorithms.

Detection of tumors on mammography images

This repository contains the code derived from the master thesis project on mammographic image generation using diffusion models.

Multi-modal deep learning with attention mechanism

Stack of REST APIs built on Flask for serving requests to MAMMORY (App), deployed on Azure with GitHub Actions (CI/CD)

Code relevant for training, evaluating, assessing, and deploying CNNs for image classification and segmentation of Digital Mammography images

This repository contains the code derived from the writing of the master thesis project on mammographic image generation using diffusion models.

Multilevel thresholding segmentation method

Breast abnormalities classification and diagnosis using TensorFlow developed for Computational Intelligence and Deep Learning course of the MSc AIDE at the University of Pisa.

Restore low-dose DBT projections using VCT software

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.

Independent evaluation of a multi-view multi-task convolutional neural network breast cancer classification model using Finnish mammography screening data

Mammography Abnormality Detector Implementing Deep Neural Networks and Achieving 96% Accuracy.

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.

Baseline for Breast Mass Detection via BI-RADS Score by Using YOLO Models

presentation of breast cancer diagnosis in mammography using the self-organizing SOM network based on the Mammographic Mass_MLR dataset

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.

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

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'.