Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography

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This repository contains the training and testing codes for the paper "Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography". For simulating dose reduction on clinical images, we used the codes available here. Also, we used a model-based (MB) restoration as a benchmark, also available here, which uses the commonly known BM3D.

Network architecture:

Some results:

Restoration of images with a dose reduction factor of 75%:

Restoration of images with a dose reduction factor of 50%:

Reference:

If you use the toolbox, we will be very grateful if you refer to this paper:

AI-based X-ray Imaging System (AXIS)
Department of Biomedical Engineering
Rensselaer Polytechnic Institute
Troy - USA

Laboratory of Computer Vision (Lavi)
Department of Electrical and Computer Engineering
São Carlos School of Engineering, University of São Paulo
São Carlos - Brazil

Low-dose Digital Mammography with Deep Learning

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Overview

Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography

Network architecture:

Some results:

Reference:

Owner

WANG-AXIS

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