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 前列腺癌检测和肺癌筛查等问题。FDA批准全幻灯片数字病理学 用于初步诊断的成像(WSIS)进一步增加了人们对人工智能的兴趣、采用和投资- 用于病理学的人工智能(AI)技术。使用患者级标签(PLL)从大型医学图像中学习具有 成为一个活跃的计算病理学研究领域。PLL如病理诊断或临床结果 是通过医疗保健业务产生的，而且往往很容易获得。与学习范式形成对比 这依赖于图像的专家注释(例如，描绘肿瘤区域)，因此是时间密集型的 而且仅限于较小的队列，使用PLL直接从WSIS进行培训将允许开发现实 训练数据集包含数以万计的受试者，这些受试者可以产生具有临床意义的模型。 库拉西。GPU加速器在推动计算深度学习方法方面发挥了重要作用 病理学工具。机器学习框架(MLF)，如Pytorch和TensorFlow，为研究人员提供了 抽象以快速开发利用GPU的模型。GPU和MLF的发展是由 对小图像的分析，因此将这些工具直接应用于WSIS或其他大型医学图像，如 体积磁共振或计算机断层扫描具有挑战性。调整医学成像问题以适应 小图像范例会导致许多折衷方案，从而导致性能不佳、实现增加 规划工作，以及增加的软件/设计复杂性(例如，基于补丁的技术或多实例 学习)。因此，通过直接处理WSI图像，从PLL开发可伸缩的ML模型 通过深度学习管道，如今在GPU上是不可行的。使用统一GPU内存或 用于克服GPU内存限制并尝试在WSI规模执行端到端培训的流方法 已经展示了优于注释或MIL的性能。然而，这些方法要么很慢(由于 次优数据移动策略)、复杂的适应/使用或高度特定于给定的网络体系结构 (限制开发和探索新体系结构的能力)。更通用、更高效、更用户友好 需要框架来发展WSI规模的深度学习。 该项目将开发一个强大的软件框架，以促进无缝开发和使用可伸缩的 ML模型，不对处理的图像大小施加任何限制，不受有限内存的阻碍 GPU中的容量。提出的SSTEP(无缝可伸缩张量表达式通过划分执行)软. Ware框架将允许可扩展和可移植的神经网络模型直接处理全高分辨率 在任何(多)GPU平台上，用于训练或推理的任意大小的图像。SSTEP将允许开发 为医疗应用专门构建的新型深度学习范例，将使开发人员能够 使用熟悉的MLF-PyTorch或TensorFlow快速创建和评估这些工具。

Project Summary/Abstract Deep learning has transformed medical image analysis by delivering clinically meaningful results on challenging problems like prostate cancer detection and lung cancer screening. FDA approval of whole-slide digital pathology imaging (WSIs) for primary diagnosis is further increasing interest, adoption, and investment in artificial intelli- gence (AI) technology for pathology. Learning from large medical images using patient-level labels (PLLs) has become an active computational pathology research area. PLLs such as pathology diagnosis or clinical outcomes are generated through healthcare operations and are often readily available. In contrast to learning paradigms that depend on the expert annotation of images (e.g., delineating tumor regions) and are therefore time-intensive and limited to smaller cohorts, training directly from WSIs using PLLs will allow the development of realistic training datasets containing tens-of-thousands of subjects that can produce models with clinically-meaningful ac- curacy. GPU accelerators have played a significant role in advancing deep learning methods for computational pathology tools. Machine Learning Frameworks (MLFs), e.g., Pytorch and TensorFlow, provide researchers with abstractions to quickly develop models that utilize GPUs. The evolution of GPUs and MLFs has been driven by the analysis of small images, and so applying these tools directly to WSIs or other large medical images like volumetric magnetic resonance or computed tomography is challenging. Adapting medical imaging problems to the small image paradigm leads to many compromises resulting in suboptimal performance, increased imple- mentation effort, and increased software/design complexity (e.g., patch based techniques or multiple instance learning). As a result, the development of scalable ML models from PLLs by directly processing WSI images through a deep learning pipeline is infeasible today on GPUs. Recent efforts that use unified GPU memory or streaming approaches to overcome GPU memory limits and attempt to perform end-to-end training at WSI scale have demonstrated superior performance to annotation or MIL. However, these approaches are either slow (due to suboptimal data movement strategies), complex to adapt/use, or highly specific to a given network architecture (limiting the ability to develop and explore new architectures). More general-purpose, efficient, and user-friendly frameworks are needed to allow the development of WSI scale deep learning. This project will develop a robust software framework to facilitate seamless development and use of scalable ML models, without the imposition of any limits on the sizes of handled images, unhindered by the limited memory capacity in GPUs. The proposed SSTEP (Seamless Scalable Tensor-Expression Execution via Partitioning) soft- ware framework will allow scalable and portable neural network models that directly process full high-resolution images of arbitrary size for training or inference, on any (multi) GPU platform. SSTEP will allow the development of novel deep learning paradigms that are purpose-built for medical applications, and will enable developers to rapidly create and evaluate these tools using familiar MLFs - PyTorch or TensorFlow.