Attention-driven Spatial Transformer Network for Abnormality Detection in Chest X-Ray Images

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

Rocha, J (Author) Other; Pereira, SC (Author) Other; Pedrosa, J (Author) Other; Aurélio Campilho (Author) FEUP; Ana Maria Mendonça (Author) FEUP

Conference proceedings International

Title: 2022 IEEE 35TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS)

Pages: 252-257

35th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

ELECTR NETWORK, JUL 21-23, 2022

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Other information

Authenticus ID: P-00X-3V0

DOI: 10.1109/cbms55023.2022.00051

Abstract (EN): Backed by more powerful computational resources and optimized training routines, deep learning models have attained unprecedented performance in extracting information from chest X-ray data. Preceding other tasks, an automated abnormality detection stage can be useful to prioritize certain exams and enable a more efficient clinical workflow. However, the presence of image artifacts such as lettering often generates a harmful bias in the classifier, leading to an increase of false positive results. Consequently, healthcare would benefit from a system that selects the thoracic region of interest prior to deciding whether an image is possibly pathologic. The current work tackles this binary classification exercise using an attention-driven and spatially unsupervised Spatial Transformer Network (STN). The results indicate that the STN achieves similar results to using YOLO-cropped images, with fewer computational expenses and without the need for localization labels. More specifically, the system is able to distinguish between normal and abnormal CheXpert images with a mean AUC of 84.22%.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6