An active learning approach for support device detection in chest radiography images

Raquel Belo; Joana Rocha; Ana Maria Mendonça; Aurélio Campilho

doi:10.1117/12.2679310

7 June 2023 An active learning approach for support device detection in chest radiography images

Raquel Belo, Joana Rocha, Ana Maria Mendonça, Aurélio Campilho

Proceedings Volume 12701, Fifteenth International Conference on Machine Vision (ICMV 2022); 127010X (2023) https://doi.org/10.1117/12.2679310
Event: Fifteenth International Conference on Machine Vision (ICMV 2022), 2022, Rome, Italy

Abstract

Deep Learning (DL) algorithms allow fast results with high accuracy in medical imaging analysis solutions. However, to achieve a desirable performance, they require large amounts of high quality data. Active Learning (AL) is a subfield of DL that aims for more efficient models requiring ideally fewer data, by selecting the most relevant information for training. CheXpert is a Chest X-Ray (CXR) dataset, containing labels for different pathologic findings, alongside a “Support Devices” (SD) label. The latter contains several misannotations, which may impact the performance of a pathology detection model. The aim of this work is the detection of SDs in CheXpert CXR images and the comparison of the resulting predictions with the original CheXpert SD annotations, using AL approaches. A subset of 10,220 images was selected, manually annotated for SDs and used in the experimentations. In the first experiment, an initial model was trained on the seed dataset (6,200 images from this subset). The second and third approaches consisted in AL random sampling and least confidence techniques. In both of these, the seed dataset was used initially, and more images were iteratively employed. Finally, in the fourth experiment, a model was trained on the full annotated set. The AL least confidence experiment outperformed the remaining approaches, presenting an AUC of 71.10% and showing that training a model with representative information is favorable over training with all labeled data. This model was used to obtain predictions, which can be useful to limit the use of SD mislabelled images in future models.

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Raquel Belo, Joana Rocha, Ana Maria Mendonça, and Aurélio Campilho "An active learning approach for support device detection in chest radiography images", Proc. SPIE 12701, Fifteenth International Conference on Machine Vision (ICMV 2022), 127010X (7 June 2023); https://doi.org/10.1117/12.2679310

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KEYWORDS

Data modeling

Chest imaging

Chest

Performance modeling

Radiography

Active learning

Image classification

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