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LAVI (Laboratory of Computer Vision)'s Projects

cmlmp icon cmlmp

Algorithms for Completed Mean Local Mapped Pattern - CMLMP

dmar2dct icon dmar2dct

Deep Learning Metal Artifact Reduction for 2D Computed Tomography

iwbi2022-imagequality icon iwbi2022-imagequality

It contains the codes for the paper "Using virtual clinical trials to assess objective image quality metrics in the task of microcalcification localization in digital mammography", submitted to the IWBI 2022 conference.

iwbi2022-psloss icon iwbi2022-psloss

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.

iwbi2024-restorationmodelobserver icon iwbi2024-restorationmodelobserver

It contains the codes for the paper "Evaluating an image restoration pipeline for digital mammography across varied radiation exposures and microcalcification sizes using model observer analysis", submitted to the IWBI 2024 conference.

machine-learning icon machine-learning

Study and implementation about deep learning models, architectures, applications and frameworks

mcinsertionpackage icon mcinsertionpackage

This is an agorithm to insert microcalcification clusters into Digital Mammograms

mcinsertionpackage-dbt icon mcinsertionpackage-dbt

Computational method to perform the artificial insertion of microcalcifications in normal mammograms

noisedecorrelation-dbt icon noisedecorrelation-dbt

This repository contains the training and testing codes for the paper "Suppressing noise correlation in digital breast tomosynthesis using convolutional neural network and virtual clinical trials", submitted to the IWBI 2022 conference

pydbt icon pydbt

This package is a python extension of the DBT toolbox from LAVI-USP

sel0629 icon sel0629

SEL 0629 - APLICAÇÃO DE MICROPROCESSADORES I

spie2022 icon spie2022

Restore low-dose DBT projections using VCT software

spie2023-imagequality icon spie2023-imagequality

It contains the codes for the paper "Assessing the impact of correlated noise in digital mammography: a virtual clinical trial", submitted to the SPIE 2023 conference.

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