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

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

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.

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

Detection of tumors on mammography images

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

Multilevel thresholding segmentation method

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

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

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.

Code for "Radio-opaque artefacts in digital mammography: Automatic detection and analysis of downstream effects" - ISBI 2025 paper

MICCAI 2024: Ordinal Learning: Longitudinal Attention Alignment Model for Predicting Time to Future Breast Cancer Events from Mammograms

Breast cancer classification using ensemble deep learning with VGG16 and ResNet50V2 on CBIS-DDSM mammogram dataset

1st-place-solution of SPR Screening Mammography Recall

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.