太阳能无线电通量以及地磁指数是太阳能活动的重要指标及其效果。耀斑和地磁风暴等极端太阳能事件可能对低地轨道中的卫星的空间环境产生负面影响。因此，预测这些空间天气指数在太空运营和科学方面具有重要意义。在这项研究中，我们提出了一种基于长期短期内存神经网络的模型，以了解时间序列数据的分布，以便使用时间序列以及太阳能图像提供空间天气指标的同时多元27天预测数据。我们展示了30-40 \％的根均方误差改进了，而仅包括使用时间序列数据的太阳能图像数据，而单独使用时间序列数据。与训练有素的深神经网络模型相比，诸如持久性和运行平均预测之类的简单基线也将与训练有素的深神经网络模型进行比较。我们还使用模型集合量化我们预测中的不确定性。

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .

Objective: Accurate visual classification of bladder tissue during Trans-Urethral Resection of Bladder Tumor (TURBT) procedures is essential to improve early cancer diagnosis and treatment. During TURBT interventions, White Light Imaging (WLI) and Narrow Band Imaging (NBI) techniques are used for lesion detection. Each imaging technique provides diverse visual information that allows clinicians to identify and classify cancerous lesions. Computer vision methods that use both imaging techniques could improve endoscopic diagnosis. We address the challenge of tissue classification when annotations are available only in one domain, in our case WLI, and the endoscopic images correspond to an unpaired dataset, i.e. there is no exact equivalent for every image in both NBI and WLI domains. Method: We propose a semi-surprised Generative Adversarial Network (GAN)-based method composed of three main components: a teacher network trained on the labeled WLI data; a cycle-consistency GAN to perform unpaired image-to-image translation, and a multi-input student network. To ensure the quality of the synthetic images generated by the proposed GAN we perform a detailed quantitative, and qualitative analysis with the help of specialists. Conclusion: The overall average classification accuracy, precision, and recall obtained with the proposed method for tissue classification are 0.90, 0.88, and 0.89 respectively, while the same metrics obtained in the unlabeled domain (NBI) are 0.92, 0.64, and 0.94 respectively. The quality of the generated images is reliable enough to deceive specialists. Significance: This study shows the potential of using semi-supervised GAN-based classification to improve bladder tissue classification when annotations are limited in multi-domain data.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

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.

使用相对比心脏磁共振成像（PC-CMR）进行的流量分析可以量化用于评估心血管功能的重要参数。该分析的重要部分是鉴定正确的CMR视图和质量控制（QC），以检测可能影响流量定量的伪像。我们提出了一个新型的基于深度学习的框架，用于对完整CMR扫描的流量进行完全自动化的分析，该框架首先使用两个顺序卷积神经网络进行这些视图选择和QC步骤，然后进行自动主动脉和肺动脉分段，以实现对量化的量化。钥匙流参数。对于观察分类和QC，获得了0.958和0.914的精度值。对于细分，骰子分数为$> $ 0.969，而平淡的altman情节表示手动和自动峰流量值之间的一致性很高。此外，我们在外部验证数据集上测试了管道，结果表明管道的鲁棒性。这项工作是使用由986例病例组成的多生临床数据进行的，表明在临床环境中使用该管道的潜力。

深度学习（DL）技术被回归问题所接受。最近在该领域发表的论文数量越来越多，包括调查和评论，表明，由于效率和具有高维数据的系统的良好精度，深层回归引起了社区的关注。但是，许多DL方法具有复杂的结构，这些结构对人类用户不易透明。访问这些模型的可解释性是解决敏感领域问题（例如网络安全系统，医疗，金融监视和工业过程）的重要因素。模糊逻辑系统（FLS）是可解释的模型，在文献中众所周知，能够通过具有成员资格学位的语言术语对复杂系统使用非线性表示，模仿了人类的思想。在可解释的人工智能的气氛中，有必要考虑开发智能模型的准确性和可解释性之间的权衡。本文旨在调查结合DL和FL的现有方法的最新方法，即深度模糊系统，以解决回归问题，配置当前在文献中尚不充分探索的主题，因此应进行全面调查。

我们研究了图结构识别的问题，即在时间序列之间恢复依赖图的图。我们将这些时间序列数据建模为线性随机网络动力学系统状态的组成部分。我们假设部分可观察性，其中仅观察到一个包含网络的节点子集的状态演变。我们设计了一个从观察到的时间序列计算的新功能向量，并证明这些特征是线性可分离的，即存在一个超平面，该超平面将与连接的节点成对相关的特征群体与与断开对相关的节点相关联。这使得可以训练各种分类器进行因果推理的功能。特别是，我们使用这些功能来训练卷积神经网络（CNN）。由此产生的因果推理机制优于最先进的W.R.T.样品复杂性。受过训练的CNN概括了结构上不同的网络（密集或稀疏）和噪声级别的轮廓。值得注意的是，他们在通过合成网络（随机图的实现）训练时也很好地概括了现实世界网络。最后，提出的方法始终以成对的方式重建图，也就是说，通过确定每对相应的时间序列中的每对节点中是否存在边缘或箭头或不存在箭头。这符合大规模系统的框架，在该系统中，网络中所有节点的观察或处理都令人难以置信。

人类智力的标志是能够构建独立的知识块，并在解决不同问题的新颖组合中重复使用它们。由于基础组合搜索，学习这种组成结构一直是人造系统的挑战。迄今为止，对构图学习的研究主要与终身学习或持续学习的工作分开进行。这项论文整合了这两条工作，以提出一个通用框架，用于终身学习功能组成结构。该框架将学习分为两个阶段：学习如何结合现有组件以吸收新的问题，并学习如何调整现有组件以解决新问题。这种分离明确处理了稳定性和灵活性之间的权衡。该论文将框架实例化成各种监督和加强学习（RL）算法。监督学习评估发现，1）组成模型改善了各种任务的终身学习，2）多阶段过程允许终身学习构图知识，而3）框架所学的组件代表了独立和可重复使用的功能。类似的RL评估表明，1）框架下的算法加速了高绩效策略的发现，而2）这些算法保留或改善了先前学习的任务的性能。论文将一个终​​身的组成RL算法扩展到了非组织设置，在该设置中，任务分布会随着时间而变化，并发现模块化允许单独跟踪对环境中不同元素的变化。本论文的最终贡献是组成RL的新基准，该基准暴露了现有方法难以发现环境的组成特性。

人工智能（AI）的主要目标是创建能够获得对世界一般理解的代理。这样的代理人将需要能够在遇到新体验时不断积累和建立知识的能力。终身或持续的学习解决了这种设置，因此代理商面临连续的问题，并且必须努力捕获解决其遇到的每项新任务所需的知识。如果代理能够以某种形式的组成表示积累知识，则可以选择性地重复使用并结合相关知识以构建新的解决方案。尽管这个简单的想法具有直观的吸引力，但关于终身学习和构图学习的文献在很大程度上已经进行了。为了促进两个领域之间桥接的发展，本文调查了他们各自的研究景观，并讨论了它们之间的现有和未来联系。