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CASS: Cross Architectural Self-Supervision for Medical Image Analysis. (arXiv:2206.04170v3 [cs.CV] UPDATED)
cs.CV updates on arXiv.org arxiv.org
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 …