Enabling Next Generation Machine Learning for Large Scale Image Analysis

项目总结/摘要 深度学习通过提供具有临床意义的结果来改变医学图像分析， 比如前列腺癌检测和肺筛查。在病理学方面，工业界正在进行重大投资- 开发用于临床实验室诊断的深度学习工具。FDA批准全载玻片数字 用于初步诊断的病理学图像（WSIs）进一步增加了兴趣、采用和投资 in this technology技术.病理学家做出的判断是许多疾病治疗的基础，但在- 病理学家之间的观察者差异很大，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，因为对病理学家服务的需求超过了 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 通过提供可重复的诊断，为人类病理学家执行"第二次读取"，自动化 提高病理学家效率的任务，并帮助普通病理学家评估具有挑战性的病例。GPU加速 在推进深度学习方法以构建计算病理学工具方面， Pytorch和Tensor等机器学习框架（MLF）为研究人员提供了抽象 来快速开发利用GPU的模型。GPU和MLF的演变是由对小规模 图像，因此这些工具不能容易地直接应用于WSI或其他大型医学图像，如三维图像， MRI或CT。使医学成像问题适应GPU和MLF支持的小图像范例 导致次优性能和增加的实现工作量和复杂性。较新的办法 使用流或"统一艾德内存"的系统允许直接分析整个WSI，并已证明其性能 优势这些方法可能速度较慢，实施复杂，并且对网络选择具有高度特定性 限制了新体系结构的探索和发展。更通用、更高效， 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。拟议 软件将包括一个性能建模器，用于估计给定网络在可用GPU加速器上的运行时间- 反对者这些策略将为医学图像的深度学习提供新的范例， 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 使用熟悉的多边基金软件包评估这些网络。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.