Paper
22 May 2020 Transfer learning in deep convolutional neural networks for detection of architectural distortion in digital mammography
Author Affiliations +
Proceedings Volume 11513, 15th International Workshop on Breast Imaging (IWBI2020); 115130N (2020) https://doi.org/10.1117/12.2564348
Event: Fifteenth International Workshop on Breast Imaging, 2020, Leuven, Belgium
Abstract
Deep learning models have reached superior results in various fields of application, but in many cases at a high cost of processing or large amount of data available. In most of them, specially in the medical field, the scarcity of training data limits the performance of these models. Among the strategies to overcome the lack of data, there is data augmentation, transfer learning and fine-tuning. In this work we compared different approaches to train deep convolutional neural network (CNN) to automatically detect architectural distortion (AD) in digital mammography. Although several computer vision based algorithms were designed to detect lesions in digital mammography, most of them perform poorly while detecting AD. We used the VGG-16 network pre-trained on ImageNet database with progressive fine-tuning to evaluate its performance on AD detection over a database of 280 images of clinical mammograms. Finally, we compared the results with a custom CNN architecture trained from scratch for the same task. Results indicated that a network with transfer learning and certain level of fine-tuning reaches the best results for the task (AUC = 0.89) compared with the other approaches, but no statistically significant difference was found between the best results using different amount of data augmentation and also compared to the custom CNN.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Arthur C. Costa, Helder C. R. Oliveira, Lucas R. Borges, and Marcelo A. C. Vieira "Transfer learning in deep convolutional neural networks for detection of architectural distortion in digital mammography", Proc. SPIE 11513, 15th International Workshop on Breast Imaging (IWBI2020), 115130N (22 May 2020); https://doi.org/10.1117/12.2564348
Lens.org Logo
CITATIONS
Cited by 3 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Mammography

Digital mammography

Data modeling

Architectural distortion

Convolutional neural networks

Breast

Computer aided diagnosis and therapy

Back to Top