CASS: Cross Architectural Self-Supervision for Medical Image Analysis

• URL: http://arxiv.org/abs/2206.04170v2
• Date: Fri, 10 Jun 2022 04:30:50 GMT
• Title: CASS: Cross Architectural Self-Supervision for Medical Image Analysis
• Authors: Pranav Singh, Elena Sizikova, Jacopo Cirrone
• Abstract summary: Cross Architectural Self-Supervision is a novel self-supervised learning approach which leverages transformers and CNN simultaneously. Compared to existing state-of-the-art self-supervised learning approaches, we empirically show CASS trained CNNs, and Transformers gained an average of 8.5% with 100% labelled data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in Deep Learning and Computer Vision have alleviated many of the bottlenecks, allowing algorithms to be label-free with better performance. Specifically, Transformers provide a global perspective of the image, which Convolutional Neural Networks (CNN) lack by design. Here we present Cross Architectural Self-Supervision, a novel self-supervised learning approach which leverages transformers and CNN simultaneously, while also being computationally accessible to general practitioners via easily available cloud services. Compared to existing state-of-the-art self-supervised learning approaches, we empirically show CASS trained CNNs, and Transformers gained an average of 8.5% with 100% labelled data, 7.3% with 10% labelled data, and 11.5% with 1% labelled data, across three diverse datasets. Notably, one of the employed datasets included histopathology slides of an autoimmune disease, a topic underrepresented in Medical Imaging and has minimal data. In addition, our findings reveal that CASS is twice as efficient as other state-of-the-art methods in terms of training time.

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