Enabling Next Generation Machine Learning for Large Scale Image Analysis

项目摘要/摘要 深度学习通过提供具有挑战性的临床有意义的结果来改变医学图像分析 前列腺癌检测和肺部筛查等问题。在病理学方面，行业正在使fi无法投资- 开发用于临床实验室诊断的深度学习工具。FDA批准全幻灯片数字 用于初步诊断的病理图像(WSIS)正进一步引起人们的兴趣、采用率和投资 在这项技术中。病理学家的判断是治疗许多疾病的基础，但在 病理学家之间的观察者变异性是显著的fi不能，错误可能导致过度治疗甚至治疗 健康的病人。病理学还面临着劳动力问题，因为对病理医生服务的需求正在超过需求 训练有素的病理学家的成长。基于深度学习的计算病理学工具可以帮助解决这些问题 通过提供可重现的诊断、为人类病理学家执行“二次读取”、自动化 任务是提高病理学家的fi效率，并帮助普通病理学家评估具有挑战性的病例。GPU加速- 创建者在推进深度学习方法以构建计算病理学工具方面发挥了重要作用，fiCan， 通过机器学习框架(MLF)，如PYTORCH和张量fl现在为研究人员提供了抽象 快速开发使用GPU的型号。GPU和MLF的发展是由小型 图像，因此这些工具不能直接应用于WSIS或其他大型医学图像，如三维图像- 局部核磁共振或CT。使医学成像问题适应由GPU和MLFS支持的小图像范例 导致性能不佳，增加了实施的工作量和复杂性。更新的方法 使用流或“Unified Memory”可以直接分析整个信息社会世界信息系统，并展示了它的性能 优势。这些方法可能很慢、实施起来很复杂，而且对网络的选择有很高的要求(fic 建筑限制了新建筑的探索和发展。更通用、更有效的EFfi和 需要用户友好的框架来开发WSI规模的深度学习。 该项目将开发自动映射共同实施的深度学习网络的技术 MLF架构到一个或多个GPU，用于任意大的输入图像和激活层。建议数 软件将包括一个性能建模器，用于在可用的GPU加速器上估计给定网络的运行时间。 超人。这些策略将使医学图像深度学习成为一种新的范式，从而使 专为医疗应用而构建的新型网络。开发人员将能够快速创建和 使用熟悉的MLF包评估这些网络。该项目将提供克服GPU的方法 内存瓶颈、将网络映射到可用GPU的调度器、与常见MLF的集成以及 使用计算病理学用例进行演示。

Project Summary/Abstract Deep learning has transformed medical image analysis by delivering clinically meaningful results on challenging problems like prostate cancer detection and lung screening. In pathology, industry is making significant invest- ments to develop deep learning tools for diagnostic use in clinical labs. FDA approval of whole-slide digital pathology images (WSIs) for use in primary diagnosis is further increasing interest, adoption, and investment in this technology. Judgments made by pathologists are the basis for the treatment of many diseases, yet in- terobserver variability among pathologists is significant, and errors can lead to overtreatment or even treatment of healthy patients. Pathology is also facing workforce issues as demand for pathologist services is outpacing growth of trained pathologists. Computational pathology tools based on deep learning can help address these problems by providing reproducible diagnoses, performing ”second reads” for human pathologists, automating tasks to improve pathologist efficiency, and helping general pathologists evaluate challenging cases. GPU accel- erators have played a significant role in advancing deep learning methods to build computational pathology tools, with machine learning frameworks (MLFs) like Pytorch and Tensorflow providing researchers with abstractions to quickly develop models that utilize GPUs. Evolution of GPUs and MLFs has been driven by analysis of small images, and so these tools cannot be easily applied directly WSIs or other large medical images like three dimen- sional MRI or CT. Adapting medical imaging problems to small image paradigms supported by GPUs and MLFs leads to suboptimal performance and increased implementation effort and complexity. More recent approaches that use streaming or ”unified memory” allow direct analysis of entire WSIs and have demonstrated performance advantages. These approaches can be slow, complex to implement, and are highly specific to a choice of network architecture which limits exploration and development of new architectures. More general-purpose, efficient, and user-friendly frameworks are required to allow the development of WSI scale deep learning. This project will develop techniques to automatically map deep learning networks implemented in common MLF architectures to one or more GPUs for arbitrarily large input images and activation layers. The proposed software will include a performance modeler to estimate the runtime of a given network on available GPU acceler- ators. These strategies will enable a new paradigm in deep learning for medical images, allowing the development of novel networks that are purpose-built for medical applications. Developers will be able to rapidly create and evaluate these networks using familiar MLF packages. This project will provide approaches to overcome GPU memory bottlenecks, a scheduler to map the network to available GPUs, integration with common MLFs, and demonstration using computational pathology use cases.