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

Detection of tumors on mammography images

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.

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

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

Multi-modal deep learning with attention mechanism

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

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

Restore low-dose DBT projections using VCT software

Multilevel thresholding segmentation method

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.

"Breast_Cropper" PyTorch model is designed for the purpose of cropping breast tissues from mammograms.

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

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

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

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.

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.