Using deep learning to discover interpretable representations for mammogram classification and explanation
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Updated
Apr 11, 2023 - Jupyter Notebook
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.
Breast abnormalities classification and diagnosis using TensorFlow developed for Computational Intelligence and Deep Learning course of the MSc AIDE at the University of Pisa.
Multilevel thresholding segmentation method
This repository contains the code derived from the writing of the master thesis project on mammographic image generation using diffusion models.
Stack of REST APIs built on Flask for serving requests to MAMMORY (App), deployed on Azure with GitHub Actions (CI/CD)
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
Official repository of "Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data"
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.
Baseline for Breast Mass Detection via BI-RADS Score by Using YOLO Models
Independent evaluation of a multi-view multi-task convolutional neural network breast cancer classification model using Finnish mammography screening data
A vision-language implementation for automated mammography reporting using CLIP (Contrastive Language-Image Pre-Training) neural network.
Licenciatura en Ciencia de Datos - Universidad del Gran Rosario
This repository contains the code derived from the master thesis project on mammographic image generation using diffusion models.
Restore low-dose DBT projections using VCT software
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.
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