Explainable rotation-invariant self-supervised representation learning

Abstract

This paper describes a method that can perform robust detection and classification in out-of-distribution rotated images in the medical domain. In real-world medical imaging tools, noise due to the rotation of the body part is frequently observed. This noise reduces the accuracy of AI-based classification and prediction models. Hence, it is important to develop models which are rotation invariant. To that end, the proposed method - RISC (rotation invariant self-supervised vision framework) addresses this issue of rotational corruption. We present state-of-the-art rotation-invariant classification results and provide explainability for the performance in the domain. The evaluation of the proposed method is carried out on real-world adversarial examples in Medical Imagery-OrganAMNIST, RetinaMNIST and PneumoniaMNIST. It is observed that RISC outperforms the rotation-affected benchmark methods by obtaining 22\%, 17\% and 2\% accuracy boost on OrganAMNIST, PneumoniaMNIST and RetinaMNIST rotated baselines respectively. Further, explainability results are demonstrated.

This methods paper describes:

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  a representation learning approach that can perform robust detection and classification in out-of-distribution rotated images in the medical domain.

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  It presents a method that incorporates self-supervised rotation invariance for correcting rotational corruptions.

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  GradCAM-based explainability for the rotational SSL pretext task and the downstream classification outcomes for the three benchmark datasets are presented