使用 3D SimCLR 和 Monte Carlo 辍学者进行3D医学图像分析自我监督学习 (Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout)

Self-supervised learning methods can be used to learn meaningful representations from unlabeled data that can be transferred to supervised downstream tasks to reduce the need for labeled data. In this paper, we propose a 3D self-supervised method that is based on the contrastive (SimCLR) method. Additionally, we show that employing Bayesian neural networks (with Monte-Carlo Dropout) during the inference phase can further enhance the results on the downstream tasks. We showcase our models on two medical imaging segmentation tasks: i) Brain Tumor Segmentation from 3D MRI, ii) Pancreas Tumor Segmentation from 3D CT. Our experimental results demonstrate the benefits of our proposed methods in both downstream data-efficiency and performance.

翻译：自我监督的学习方法可以用来从未贴标签的数据中学习有意义的表述方法,这些数据可以转移到监督的下游任务,以减少对标签数据的需求。在本文中,我们提议了一种基于对比(SimCLR)法的3D自我监督方法。此外,我们表明,在推论阶段使用Bayesian神经网络(与Monte-Carlo Droot一起)可以进一步加强下游任务的结果。我们展示了我们关于两项医学成像分割任务的模式:一) 3D MRI的脑图象分割,二) 3D CT的Pancreas Tumor分割。我们的实验结果显示了我们所提议的方法在下游数据效率和性能两方面的好处。