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 \textbf{C}ross \textbf{A}rchitectural - \textbf{S}elf \textbf{S}upervision , 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.

本论文介绍Cross Architectural - Self Supervision (CASS),一种同时利用Transformer和CNN的新型自我监督学习方法。实验证明，与现有自我监督学习方法相比，CASS训练的CNN和Transformer在四个不同的数据集上提高了平均3.8%（有1%的标注数据），5.9%（有10%的标注数据）和10.13%（有100%的标注数据），同时花费了少达69％的时间。同时，CASS对批量大小和训练时期的改变更具有鲁棒性。

CASS: 跨结构自监督用于医学图像分析