论文标题
部分可观测时空混沌系统的无模型预测
BYOLMed3D: Self-Supervised Representation Learning of Medical Videos using Gradient Accumulation Assisted 3D BYOL Framework
论文作者
论文摘要
医学图像分析的申请遭受了医学专家正确注释的大量数据的严重短缺。监督的学习算法需要大量平衡数据才能学习稳健的表示。经常有监督的学习算法需要各种技术来处理不平衡的数据。另一方面，自我监督的学习算法在数据中具有强大的不平衡性，并且能够学习强大的表示。在这项工作中，我们使用梯度积累技术训练3D BYOL自制模型，以在自我监督算法中通常需要的批处理中处理大量样品。据我们所知，这项工作是该领域中的第一个工作之一。我们比较了通过当代自我监管的预训练预训练方法以及用动力学400预训练的预训练的RESNET3D-18比较通过实验在ACL泪受伤检测的下游任务中获得的结果。从下游任务实验中，很明显，所提出的框架的表现优于现有基准。
Applications on Medical Image Analysis suffer from acute shortage of large volume of data properly annotated by medical experts. Supervised Learning algorithms require a large volumes of balanced data to learn robust representations. Often supervised learning algorithms require various techniques to deal with imbalanced data. Self-supervised learning algorithms on the other hand are robust to imbalance in the data and are capable of learning robust representations. In this work, we train a 3D BYOL self-supervised model using gradient accumulation technique to deal with the large number of samples in a batch generally required in a self-supervised algorithm. To the best of our knowledge, this work is one of the first of its kind in this domain. We compare the results obtained through our experiments in the downstream task of ACL Tear Injury detection with the contemporary self-supervised pre-training methods and also with ResNet3D-18 initialized with the Kinetics-400 pre-trained weights. From the downstream task experiments, it is evident that the proposed framework outperforms the existing baselines.
