Graph Node Based Interpretability Guided Sample Selection for Active Learning.

Mahapatra, Dwarikanath; Poellinger, Alexander; Reyes, Mauricio (2023). Graph Node Based Interpretability Guided Sample Selection for Active Learning. IEEE transactions on medical imaging, 42(3), pp. 661-673. Institute of Electrical and Electronics Engineers IEEE 10.1109/TMI.2022.3215017

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While supervised learning techniques have demonstrated state-of-the-art performance in many medical image analysis tasks, the role of sample selection is important. Selecting the most informative samples contributes to the system attaining optimum performance with minimum labeled samples, which translates to fewer expert interventions and cost. Active Learning (AL) methods for informative sample selection are effective in boosting performance of computer aided diagnosis systems when limited labels are available. Conventional approaches to AL have mostly focused on the single label setting where a sample has only one disease label from the set of possible labels. These approaches do not perform optimally in the multi-label setting where a sample can have multiple disease labels (e.g. in chest X-ray images). In this paper we propose a novel sample selection approach based on graph analysis to identify informative samples in a multi-label setting. For every analyzed sample, each class label is denoted as a separate node of a graph. Building on findings from interpretability of deep learning models, edge interactions in this graph characterize similarity between corresponding interpretability saliency map model encodings. We explore different types of graph aggregation to identify informative samples for active learning. We apply our method to public chest X-ray and medical image datasets, and report improved results over state-of-the-art AL techniques in terms of model performance, learning rates, and robustness.

Item Type:

Journal Article (Original Article)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology

UniBE Contributor:

Pöllinger, Alexander, Reyes, Mauricio

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health

ISSN:

0278-0062

Publisher:

Institute of Electrical and Electronics Engineers IEEE

Language:

English

Submitter:

Maria de Fatima Henriques Bernardo

Date Deposited:

10 Nov 2022 13:38

Last Modified:

09 Apr 2023 00:12

Publisher DOI:

10.1109/TMI.2022.3215017

PubMed ID:

36240033

BORIS DOI:

10.48350/174648

URI:

https://boris.unibe.ch/id/eprint/174648

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