Deep Feature-Based Automated Chest Radiography Compliance Assessment

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

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

Conference proceedings International

Title: 2023 IEEE 7TH PORTUGUESE MEETING ON BIOENGINEERING, ENBENG

Pages: 64-67

IEEE 7th Portuguese Meeting on Bioengineering (ENBENG)

Porto, PORTUGAL, JUN 22-23, 2023

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Other information

Authenticus ID: P-00Y-P07

DOI: 10.1109/enbeng58165.2023.10175341

Abstract (EN): Chest radiography is one of the most common imaging exams, but its interpretation is often challenging and timeconsuming, which has motivated the development of automated tools for pathology/abnormality detection. Deep learning models trained on large-scale chest X-ray datasets have shown promising results but are highly dependent on the quality of the data. However, these datasets often contain incorrect metadata and non-compliant or corrupted images. These inconsistencies are ultimately incorporated in the training process, impairing the validity of the results. In this study, a novel approach to detect non-compliant images based on deep features extracted from a patient position classification model and a pre-trained VGG16 model are proposed. This method is applied to CheXpert, a widely used public dataset. From a pool of 100 images, it is shown that the deep feature-based methods based on a patient position classification model are able to retrieve a larger number of non-compliant images (up to 81% of non-compliant images), when compared to the same methods but based on a pretrained VGG16 (up to 73%) and the state of the art uncertainty-based method (50%).

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4