Explainability Guided COVID-19 Detection in CT Scans

Ameen Ali, Tal Shaharabany, Lior Wolf

Research output: Contribution to journalConference articlepeer-review

Abstract

Radiological examination of chest CT is an effective method for screening COVID-19 cases. In this work, we overcome three challenges in the automation of this process: (i) the limited number of supervised positive cases, (ii) the lack of region-based supervision, and (iii) variability across acquisition sites. These challenges are met by incorporating a recent augmentation solution called SnapMix, a novel explainability-driven contrastive loss for patch embedding, and by performing test-time augmentation that masks out the most relevant patches in order to analyse the prediction stability. The three techniques are complementary and are all based on utilizing the heatmaps produced by the Class Activation Mapping (CAM) explainability method. State-of-the-art performance is obtained on three different datasets for COVID detection in CT scans.

Original languageEnglish
Pages (from-to)5-21
Number of pages17
JournalProceedings of Machine Learning Research
Volume172
StatePublished - 2022
Event5th International Conference on Medical Imaging with Deep Learning, MIDL 2022 - Zurich, Switzerland
Duration: 6 Jul 20228 Jul 2022

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