我们为从嘈杂和稀疏的相位对比度磁共振信号重建速度场的物理学压缩传感（图片）方法。该方法解决了逆向纳维尔的边界值问题，这使我们可以共同重建和分割速度场，同时推断隐藏量（例如流体力压力和壁剪应力）。使用贝叶斯框架，我们通过以高斯随机字段的形式引入有关未知参数的先验信息来使问题正常。使用Navier-Stokes问题，基于能量的分割功能，并要求重建与$ K $ -SPACE信号一致。我们创建了一种解决此重建问题的算法，并通过收敛喷嘴测试流量的噪声和稀疏$ K $空间信号。我们发现该方法能够从稀疏采样（15％$ k $ - 空间覆盖范围），低（$ \ sim $$ 10 $ 10 $）信噪比（SNR）信号（SNR）信号和速度区域重建和细分速度字段。重建的速度场与来自相同流量的全部采样（100％$ k $ - 空间覆盖范围）高（$> 40 $）SNR信号进行了很好的比较。

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.

The world currently offers an abundance of data in multiple domains, from which we can learn reinforcement learning (RL) policies without further interaction with the environment. RL agents learning offline from such data is possible but deploying them while learning might be dangerous in domains where safety is critical. Therefore, it is essential to find a way to estimate how a newly-learned agent will perform if deployed in the target environment before actually deploying it and without the risk of overestimating its true performance. To achieve this, we introduce a framework for safe evaluation of offline learning using approximate high-confidence off-policy evaluation (HCOPE) to estimate the performance of offline policies during learning. In our setting, we assume a source of data, which we split into a train-set, to learn an offline policy, and a test-set, to estimate a lower-bound on the offline policy using off-policy evaluation with bootstrapping. A lower-bound estimate tells us how good a newly-learned target policy would perform before it is deployed in the real environment, and therefore allows us to decide when to deploy our learned policy.

Supervised machine learning-based medical image computing applications necessitate expert label curation, while unlabelled image data might be relatively abundant. Active learning methods aim to prioritise a subset of available image data for expert annotation, for label-efficient model training. We develop a controller neural network that measures priority of images in a sequence of batches, as in batch-mode active learning, for multi-class segmentation tasks. The controller is optimised by rewarding positive task-specific performance gain, within a Markov decision process (MDP) environment that also optimises the task predictor. In this work, the task predictor is a segmentation network. A meta-reinforcement learning algorithm is proposed with multiple MDPs, such that the pre-trained controller can be adapted to a new MDP that contains data from different institutes and/or requires segmentation of different organs or structures within the abdomen. We present experimental results using multiple CT datasets from more than one thousand patients, with segmentation tasks of nine different abdominal organs, to demonstrate the efficacy of the learnt prioritisation controller function and its cross-institute and cross-organ adaptability. We show that the proposed adaptable prioritisation metric yields converging segmentation accuracy for the novel class of kidney, unseen in training, using between approximately 40\% to 60\% of labels otherwise required with other heuristic or random prioritisation metrics. For clinical datasets of limited size, the proposed adaptable prioritisation offers a performance improvement of 22.6\% and 10.2\% in Dice score, for tasks of kidney and liver vessel segmentation, respectively, compared to random prioritisation and alternative active sampling strategies.

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.

To date, the best-performing blind super-resolution (SR) techniques follow one of two paradigms: A) generate and train a standard SR network on synthetic low-resolution - high-resolution (LR - HR) pairs or B) attempt to predict the degradations an LR image has suffered and use these to inform a customised SR network. Despite significant progress, subscribers to the former miss out on useful degradation information that could be used to improve the SR process. On the other hand, followers of the latter rely on weaker SR networks, which are significantly outperformed by the latest architectural advancements. In this work, we present a framework for combining any blind SR prediction mechanism with any deep SR network, using a metadata insertion block to insert prediction vectors into SR network feature maps. Through comprehensive testing, we prove that state-of-the-art contrastive and iterative prediction schemes can be successfully combined with high-performance SR networks such as RCAN and HAN within our framework. We show that our hybrid models consistently achieve stronger SR performance than both their non-blind and blind counterparts. Furthermore, we demonstrate our framework's robustness by predicting degradations and super-resolving images from a complex pipeline of blurring, noise and compression.

卵巢癌是最致命的妇科恶性肿瘤。该疾病在早期阶段最常是无症状的，其诊断依赖于经阴道超声图像的专家评估。超声是表征附加质量的一线成像方式，它需要大量的专业知识，其分析是主观的和劳动的，因此易于误差。因此，在临床实践中需要进行自动化的过程，以促进和标准化扫描评估。使用监督的学习，我们证明了附加质量的分割是可能的，但是，患病率和标签不平衡限制了代表性不足的类别的性能。为了减轻这种情况，我们应用了一种新颖的病理学数据合成器。我们通过使用Poisson图像编辑将较少常见的质量整合到其他样品中，从而创建及其相应的地面真实分割的合成医学图像。我们的方法在所有班级中都取得了最佳性能，包括与NNU-NET基线方法相比，提高了多达8％。

通过比较算法性能，公共攀爬的攀岩可以大大加速ML研究。但是，它需要与任务相关的指标。对于涉及寄生虫负荷的疾病，例如疟疾和被忽视的热带疾病（NTDS），例如血吸虫病，目前在ML论文中报道的指标（例如AUC，F1分数）不适合临床任务。结果，爬山系统并没有使解决这些严重疾病的解决方案取得进展。本文借鉴了疟疾和NTD的示例，在当前的ML实践中强调了两个差距，并提出了改进的方法：（i）我们描述了ML开发的方面，尤其是性能指标，需要将其牢固地基于临床用途案例。 ，我们提供获取此领域知识的方法。 （ii）我们详细描述了绩效指标，以指导涉及寄生虫负荷的疾病的ML模型的开发。我们强调了患者级别的观点，室内变异性，假阳性率，检测限制和不同类型的错误的重要性。我们还讨论了在这种情况下常用的ROC曲线和AUC的问题。

医学成像中各种各样的分布和分布数据使通用异常检测成为一项艰巨的任务。最近，已经开发了许多自我监督的方法，这些方法是对健康数据的端到端模型，并具有合成异常的增强。但是，很难比较这些方法，因为尚不清楚绩效的收益是从任务本身还是围绕其培训管道来进行的。也很难评估一项任务是否可以很好地通用通用异常检测，因为它们通常仅在有限的异常范围内进行测试。为了协助这一点，我们开发了NOOD，该框架适应NNU-NET，以比较自我监督的异常定位方法。通过将综合，自我监督的任务隔离在其余培训过程中，我们对任务进行了更忠实的比较，同时还可以快速简便地评估给定数据集的工作流程。使用此功能，我们实施了当前的最新任务，并在具有挑战性的X射线数据集上对其进行了评估。

免疫反应是一个动态过程，通过该过程，身体决定抗原是自我还是非自然。这种动态过程的状态由构成该决策过程的炎症和监管参与者的相对平衡和种群定义。免疫疗法的目的，例如因此，类风湿关节炎（RA）是为了使免疫状态偏向于监管参与者，从而在反应中关闭自身免疫性途径。尽管有几种已知的免疫疗法方法，但治疗的有效性将取决于这种干预措施如何改变该状态的演变。不幸的是，此过程不仅取决于该过程的动力学，而且是在干预时的系统状态决定的 - 这种状态在应用治疗之前很难确定即使不是不可能的状态。