Explainability Guided COVID-19 Detection in CT Scans

Ameen Ali Ali, Tal Shaharabany, Lior Wolf

Keywords: Explainability, Image Classification, Mixup Augmentation

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 analyze 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.

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Paper Type: both

Primary Subject Area: Detection and Diagnosis

Secondary Subject Area: Image Synthesis

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