胰腺中的癌前囊肿或肿瘤的早期检测，即，导管内乳头状粘膜肿瘤（IPMN）是一项具有挑战性且复杂的任务，它可能导致更有利的结果。一旦检测到，还必须准确地对IPMN进行评分，因为低风险IPMN可以在监视计划下进行，而高危IPMN必须在变成癌症之前先手术切除。 IPMN分类的当前标准（Fukuoka等）显示出明显的操作员内和跨操作员变异性，除了容易出错，使适当的诊断不可靠。通过深度学习范式在人工智能方面的既定进展可能为有效支持胰腺癌的医疗决策提供了关键工具。在这项工作中，我们通过提出一种基于AI的新型IPMN分类器来遵循这一趋势，该分类器利用了Transformer网络最近在包括视觉的各种任务（包括视觉的任务）上概括的最新成功。我们特别表明，我们的基于变压器的模型比标准卷积神经网络更好地利用预训练，从而支持视觉中构建的构造统一性，包括医学图像域，并可以更好地解释获得的结果。

Wearable sensors for measuring head kinematics can be noisy due to imperfect interfaces with the body. Mouthguards are used to measure head kinematics during impacts in traumatic brain injury (TBI) studies, but deviations from reference kinematics can still occur due to potential looseness. In this study, deep learning is used to compensate for the imperfect interface and improve measurement accuracy. A set of one-dimensional convolutional neural network (1D-CNN) models was developed to denoise mouthguard kinematics measurements along three spatial axes of linear acceleration and angular velocity. The denoised kinematics had significantly reduced errors compared to reference kinematics, and reduced errors in brain injury criteria and tissue strain and strain rate calculated via finite element modeling. The 1D-CNN models were also tested on an on-field dataset of college football impacts and a post-mortem human subject dataset, with similar denoising effects observed. The models can be used to improve detection of head impacts and TBI risk evaluation, and potentially extended to other sensors measuring kinematics.

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.

We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on neuron shapes. In neuroscience research, the skeleton representation is often used as a compact and abstract representation of neuron shapes. However, existing methods are limited to getting and analyzing "curve" skeletons which can only be applied for tubular shapes. This paper presents a 3D neuron morphology analysis method for more general and complex neuron shapes. First, we introduce the concept of skeleton mesh to represent general neuron shapes and propose a novel method for computing mesh representations from 3D surface point clouds. A skeleton graph is then obtained from skeleton mesh and is used to extract sub-cellular features. Finally, an unsupervised learning method is used to embed the skeleton graph for neuron classification. Extensive experiment results are provided and demonstrate the robustness of our method to analyze neuron morphology.

Manually analyzing spermatozoa is a tremendous task for biologists due to the many fast-moving spermatozoa, causing inconsistencies in the quality of the assessments. Therefore, computer-assisted sperm analysis (CASA) has become a popular solution. Despite this, more data is needed to train supervised machine learning approaches in order to improve accuracy and reliability. In this regard, we provide a dataset called VISEM-Tracking with 20 video recordings of 30s of spermatozoa with manually annotated bounding-box coordinates and a set of sperm characteristics analyzed by experts in the domain. VISEM-Tracking is an extension of the previously published VISEM dataset. In addition to the annotated data, we provide unlabeled video clips for easy-to-use access and analysis of the data. As part of this paper, we present baseline sperm detection performances using the YOLOv5 deep learning model trained on the VISEM-Tracking dataset. As a result, the dataset can be used to train complex deep-learning models to analyze spermatozoa. The dataset is publicly available at https://zenodo.org/record/7293726.

Multi-agent artificial intelligence research promises a path to develop intelligent technologies that are more human-like and more human-compatible than those produced by "solipsistic" approaches, which do not consider interactions between agents. Melting Pot is a research tool developed to facilitate work on multi-agent artificial intelligence, and provides an evaluation protocol that measures generalization to novel social partners in a set of canonical test scenarios. Each scenario pairs a physical environment (a "substrate") with a reference set of co-players (a "background population"), to create a social situation with substantial interdependence between the individuals involved. For instance, some scenarios were inspired by institutional-economics-based accounts of natural resource management and public-good-provision dilemmas. Others were inspired by considerations from evolutionary biology, game theory, and artificial life. Melting Pot aims to cover a maximally diverse set of interdependencies and incentives. It includes the commonly-studied extreme cases of perfectly-competitive (zero-sum) motivations and perfectly-cooperative (shared-reward) motivations, but does not stop with them. As in real-life, a clear majority of scenarios in Melting Pot have mixed incentives. They are neither purely competitive nor purely cooperative and thus demand successful agents be able to navigate the resulting ambiguity. Here we describe Melting Pot 2.0, which revises and expands on Melting Pot. We also introduce support for scenarios with asymmetric roles, and explain how to integrate them into the evaluation protocol. This report also contains: (1) details of all substrates and scenarios; (2) a complete description of all baseline algorithms and results. Our intention is for it to serve as a reference for researchers using Melting Pot 2.0.

噪声的去除或取消对成像和声学具有广泛的应用。在日常生活中，Denoising甚至可能包括对地面真理不忠的生成方面。但是，对于科学应用，denoing必须准确地重现地面真相。在这里，我们展示了如何通过深层卷积神经网络来定位数据，从而以定量精度出现弱信号。特别是，我们研究了晶体材料的X射线衍射。我们证明，弱信号是由电荷排序引起的，在嘈杂的数据中微不足道的信号，在DeNo的数据中变得可见和准确。通过对深度神经网络的监督培训，具有成对的低噪声数据，可以通过监督培训来实现这一成功。这样，神经网络就可以了解噪声的统计特性。我们证明，使用人造噪声（例如泊松和高斯）不会产生这种定量准确的结果。因此，我们的方法说明了一种实用的噪声过滤策略，可以应用于具有挑战性的获取问题。

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。

本文提出了一种延时3D细胞分析的方法。具体而言，我们考虑了准确定位和定量分析亚细胞特征的问题，以及从延时3D共聚焦细胞图像堆栈跟踪单个细胞的问题。细胞的异质性和多维图像的体积提出了对细胞形态发生和发育的完全自动化分析的主要挑战。本文是由路面细胞生长过程和构建定量形态发生模型的动机。我们提出了一种基于深度特征的分割方法，以准确检测和标记每个细胞区域。基于邻接图的方法用于提取分段细胞的亚细胞特征。最后，提出了使用多个单元格特征的基于强大的图形跟踪算法在不同的时间实例中关联单元格。提供了广泛的实验结果，并证明了所提出的方法的鲁棒性。该代码可在GitHub上获得，该方法可通过Bisque Portal作为服务可用。

张量火车的分解因其高维张量的简洁表示，因此在机器学习和量子物理学中广泛使用，克服了维度的诅咒。交叉近似 - 从近似形式开发用于从一组选定的行和列中表示矩阵，这是一种有效的方法，用于构建来自其少数条目的张量的张量列器分解。虽然张量列车交叉近似在实际应用中取得了显着的性能，但迄今为止缺乏其理论分析，尤其是在近似误差方面的理论分析。据我们所知，现有结果仅提供元素近似精度的保证，这会导致扩展到整个张量时的束缚非常松。在本文中，我们通过提供精确测量和嘈杂测量的整个张量来保证准确性来弥合这一差距。我们的结果说明了选定子观察器的选择如何影响交叉近似的质量，并且模型误差和/或测量误差引起的近似误差可能不会随着张量的顺序而指数增长。这些结果通过数值实验来验证，并且可能对高阶张量的交叉近似值（例如在量子多体状态的描述中遇到的）具有重要意义。