Deep learning has gained a significant popularity in recent years thanks to its tremendous success across a wide range of relevant fields of applications, including medical image analysis domain in particular. Although convolutional neural networks (CNNs) based medical applications have been providing powerful solutions and revolutionizing medicine, efficiently training of CNNs models is a tedious and challenging task. It is a computationally intensive process taking long time and rare system resources, which represents a significant hindrance to scientific research progress. In order to address this challenge, we propose in this article, R2D2, a scalable intuitive deep learning toolkit for medical imaging semantic segmentation. To the best of our knowledge, the present work is the first that aims to tackle this issue by offering a novel distributed versions of two well‐known and widely used CNN segmentation architectures [ie, fully convolutional network (FCN) and U‐Net]. We introduce the design and the core building blocks of R2D2. We further present and analyze its experimental evaluation results on two different concrete medical imaging segmentation use cases. R2D2 achieves up to 17.5× and 10.4× speedup than single‐node based training of U‐Net and FCN, respectively, with a negligible, though still unexpected segmentation accuracy loss. R2D2 offers not only an empirical evidence and investigates in‐depth the latest published works but also it facilitates and significantly reduces the effort required by researchers to quickly prototype and easily discover cutting‐edge CNN configurations and architectures.

中文翻译：

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R2D2：用于医学影像分割的可扩展深度学习工具包

近年来，深度学习在广泛的相关应用领域（尤其是医学图像分析领域）取得了巨大成功，因此获得了极大的欢迎。尽管基于卷积神经网络 (CNN) 的医学应用程序一直在提供强大的解决方案和革命性的医学，但有效地训练 CNN 模型是一项繁琐且具有挑战性的任务。这是一个计算密集型的过程，需要很长时间和稀有的系统资源，这对科学研究进展构成了重大阻碍。为了应对这一挑战，我们在本文中提出了 R2D2，这是一种用于医学成像语义分割的可扩展的直观深度学习工具包。据我们所知，目前的工作是第一个旨在通过提供两个众所周知且广泛使用的 CNN 分割架构 [即，完全卷积网络 (FCN) 和 U-Net] 的新型分布式版本来解决这个问题的工作。我们介绍了 R2D2 的设计和核心构建块。我们进一步介绍并分析了其在两个不同的具体医学成像分割用例上的实验评估结果。R2D2 分别比基于单节点的 U-Net 和 FCN 训练实现了高达 17.5 倍和 10.4 倍的加速，尽管仍然出乎意料的分割精度损失可以忽略不计。