深神经网络实施了一系列逐层操作，每个操作都相对容易理解，但是总的总体计算通常很难理解。我们开发了一个简单的想法，可以解释有用表示的逐层结构：每一层的作用是重新格式化信息以减少目标输出的“距离”。我们通过利用最近的指标代表性相似性的工作来形式化“距离”的直观概念，并展示它如何导致几何概念的丰富空间。通过此框架，深度神经网络实施的层计算可以被视为高维表示空间中的路径。我们开发工具以在距离，角度和大地学方面表征这些几何形状。然后，我们提出在CIFAR-10训练的残留网络的三组问题：（1）路径的直线程度如何，以及每层对目标有何贡献？ （2）这些特性如何在培训上出现？ （3）更广泛的网络与更深的网络采取的路径有多相似？我们通过勾勒出其他方式来结论，这种代表性几何形状可用于理解和解释网络培训，或者规定改善网络体系结构以适合任务。

Artificial Intelligence (AI) has become commonplace to solve routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload on radiologists is steadily increasing. We project that the gap between the number of imaging exams and the number of expert radiologist readers required to cover this increase will continue to expand, consequently introducing a demand for AI-based tools that improve the efficiency with which radiologists can comfortably interpret these exams. AI has been shown to improve efficiency in medical-image generation, processing, and interpretation, and a variety of such AI models have been developed across research labs worldwide. However, very few of these, if any, find their way into routine clinical use, a discrepancy that reflects the divide between AI research and successful AI translation. To address the barrier to clinical deployment, we have formed MONAI Consortium, an open-source community which is building standards for AI deployment in healthcare institutions, and developing tools and infrastructure to facilitate their implementation. This report represents several years of weekly discussions and hands-on problem solving experience by groups of industry experts and clinicians in the MONAI Consortium. We identify barriers between AI-model development in research labs and subsequent clinical deployment and propose solutions. Our report provides guidance on processes which take an imaging AI model from development to clinical implementation in a healthcare institution. We discuss various AI integration points in a clinical Radiology workflow. We also present a taxonomy of Radiology AI use-cases. Through this report, we intend to educate the stakeholders in healthcare and AI (AI researchers, radiologists, imaging informaticists, and regulators) about cross-disciplinary challenges and possible solutions.

We develop a Bayesian semi-parametric model for the estimating the impact of dynamic treatment rules on survival among patients diagnosed with pediatric acute myeloid leukemia (AML). The data consist of a subset of patients enrolled in the phase III AAML1031 clinical trial in which patients move through a sequence of four treatment courses. At each course, they undergo treatment that may or may not include anthracyclines (ACT). While ACT is known to be effective at treating AML, it is also cardiotoxic and can lead to early death for some patients. Our task is to estimate the potential survival probability under hypothetical dynamic ACT treatment strategies, but there are several impediments. First, since ACT was not randomized in the trial, its effect on survival is confounded over time. Second, subjects initiate the next course depending on when they recover from the previous course, making timing potentially informative of subsequent treatment and survival. Third, patients may die or drop out before ever completing the full treatment sequence. We develop a generative Bayesian semi-parametric model based on Gamma Process priors to address these complexities. At each treatment course, the model captures subjects' transition to subsequent treatment or death in continuous time under a given rule. A g-computation procedure is used to compute a posterior over potential survival probability that is adjusted for time-varying confounding. Using this approach, we conduct posterior inference for the efficacy of hypothetical treatment rules that dynamically modify ACT based on evolving cardiac function.

Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.

The SNMMI Artificial Intelligence (SNMMI-AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD on March 21-22, 2022. It brought together various community members and stakeholders from academia, healthcare, industry, patient representatives, and government (NIH, FDA), and considered various key themes to envision and facilitate a bright future for routine, trustworthy use of AI in nuclear medicine. In what follows, essential issues, challenges, controversies and findings emphasized in the meeting are summarized.

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

Testing the significance of a variable or group of variables $X$ for predicting a response $Y$, given additional covariates $Z$, is a ubiquitous task in statistics. A simple but common approach is to specify a linear model, and then test whether the regression coefficient for $X$ is non-zero. However, when the model is misspecified, the test may have poor power, for example when $X$ is involved in complex interactions, or lead to many false rejections. In this work we study the problem of testing the model-free null of conditional mean independence, i.e. that the conditional mean of $Y$ given $X$ and $Z$ does not depend on $X$. We propose a simple and general framework that can leverage flexible nonparametric or machine learning methods, such as additive models or random forests, to yield both robust error control and high power. The procedure involves using these methods to perform regressions, first to estimate a form of projection of $Y$ on $X$ and $Z$ using one half of the data, and then to estimate the expected conditional covariance between this projection and $Y$ on the remaining half of the data. While the approach is general, we show that a version of our procedure using spline regression achieves what we show is the minimax optimal rate in this nonparametric testing problem. Numerical experiments demonstrate the effectiveness of our approach both in terms of maintaining Type I error control, and power, compared to several existing approaches.

当植物天然产物与药物共容纳时，就会发生药代动力学天然产物 - 药物相互作用（NPDIS）。了解NPDI的机制是防止不良事件的关键。我们构建了一个知识图框架NP-KG，作为迈向药代动力学NPDIS的计算发现的一步。 NP-KG是一个具有生物医学本体论，链接数据和科学文献的全文，由表型知识翻译框架和语义关系提取系统，SEMREP和集成网络和动态推理组成的构建的科学文献的全文。通过路径搜索和元路径发现对药代动力学绿茶和kratom-prug相互作用的案例研究评估NP-KG，以确定与地面真实数据相比的一致性和矛盾信息。完全集成的NP-KG由745,512个节点和7,249,576个边缘组成。 NP-KG的评估导致了一致（绿茶的38.98％，kratom的50％），矛盾（绿茶的15.25％，21.43％，Kratom的21.43％），同等和矛盾的（15.25％）（21.43％，21.43％，21.43％ kratom）信息。几种声称的NPDI的潜在药代动力学机制，包括绿茶 - 茶氧化烯，绿茶 - 纳多洛尔，Kratom-Midazolam，Kratom-Quetiapine和Kratom-Venlafaxine相互作用，与已出版的文献一致。 NP-KG是第一个将生物医学本体论与专注于天然产品的科学文献的全文相结合的公斤。我们证明了NP-KG在鉴定涉及酶，转运蛋白和药物的药代动力学相互作用的应用。我们设想NP-KG将有助于改善人机合作，以指导研究人员将来对药代动力学NPDIS进行研究。 NP-KG框架可在https://doi.org/10.5281/zenodo.6814507和https://github.com/sanyabt/np-kg上公开获得。

通用数据模型解决了标准化电子健康记录（EHR）数据的许多挑战，但无法将其集成深度表型所需的资源。开放的生物学和生物医学本体论（OBO）铸造本体论提供了可用于生物学知识的语义计算表示，并能够整合多种生物医学数据。但是，将EHR数据映射到OBO Foundry本体论需要大量的手动策展和域专业知识。我们介绍了一个框架，用于将观察性医学成果合作伙伴关系（OMOP）标准词汇介绍给OBO铸造本体。使用此框架，我们制作了92,367条条件，8,615种药物成分和10,673个测量结果的映射。域专家验证了映射准确性，并且在24家医院进行检查时，映射覆盖了99％的条件和药物成分和68％的测量结果。最后，我们证明OMOP2OBO映射可以帮助系统地识别可能受益于基因检测的未诊断罕见病患者。

贝叶斯优化提供了一种优化昂贵黑匣子功能的有效方法。它最近已应用于流体动力学问题。本文研究并在一系列合成测试函数上从经验上比较了常见的贝叶斯优化算法。它研究了采集函数和训练样本数量的选择，采集功能的精确计算以及基于蒙特卡洛的方法以及单点和多点优化。该测试功能被认为涵盖了各种各样的挑战，因此是理想的测试床，以了解贝叶斯优化的性能，并确定贝叶斯优化表现良好和差的一般情况。这些知识可以用于应用程序中，包括流体动力学的知识，这些知识是未知的。这项调查的结果表明，要做出的选择与相对简单的功能不相关，而乐观的采集功能（例如上限限制）应首选更复杂的目标函数。此外，蒙特卡洛方法的结果与分析采集函数的结果相当。在目标函数允许并行评估的情况下，多点方法提供了更快的替代方法，但它可能需要进行更多的客观函数评估。