超声检查的诊断准确性提高仍然是一个重要目标。在这项研究中，我们提出了一种基于生物物理特征的机器学习方法，用于乳腺癌检测，以改善基准深度学习算法以外的性能，并提供一张颜色的覆盖层覆盖层的视觉图，这些视觉图是病变中恶性肿瘤的可能性。该总体框架称为特定疾病的成像。以前，分别利用改良的完全卷积网络和改良的Googlenet对150个乳房病变进行了细分和分类。在这项研究中，在轮廓病变中进行了多参数分析。从基于生物物理和形态学模型的超声射频，包膜和对数压缩数据中提取特征。带有高斯内核的支持向量机构建了非线性超平面，我们计算了多参数空间中每个特征的超平面和数据点之间的距离。距离可以定量评估病变，并提出颜色编码并覆盖在B模式图像上的恶性肿瘤的可能性。对体内患者数据进行了培训和评估。在我们的研究中，最常见类型和大小的乳腺病变的总体准确性超过98.0％，分类为0.98，而接收器操作特征曲线下的区域的总体准确性比放射科医生的性能和深度学习系统更精确。此外，概率与BI RAD之间的相关性实现了预测乳腺癌的定量指南。因此，我们预计所提出的框架可以帮助放射科医生实现更准确，方便的乳腺癌分类和检测。

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.

Objective: Social Determinants of Health (SDOH) influence personal health outcomes and health systems interactions. Health systems capture SDOH information through structured data and unstructured clinical notes; however, clinical notes often contain a more comprehensive representation of several key SDOH. The objective of this work is to assess the SDOH information gain achievable by extracting structured semantic representations of SDOH from the clinical narrative and combining these extracted representations with available structured data. Materials and Methods: We developed a natural language processing (NLP) information extraction model for SDOH that utilizes a deep learning entity and relation extraction architecture. In an electronic health record (EHR) case study, we applied the SDOH extractor to a large existing clinical data set with over 200,000 patients and 400,000 notes and compared the extracted information with available structured data. Results: The SDOH extractor achieved 0.86 F1 on a withheld test set. In the EHR case study, we found 19\% of current tobacco users, 10\% of drug users, and 32\% of homeless patients only include documentation of these risk factors in the clinical narrative. Conclusions: Patients who are at-risk for negative health outcomes due to SDOH may be better served if health systems are able to identify SDOH risk factors and associated social needs. Structured semantic representations of text-encoded SDOH information can augment existing structured, and this more comprehensive SDOH representation can assist health systems in identifying and addressing social needs.

By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance. While this capability has been demonstrated in other fields such as computer vision, natural language processing or speech recognition, it remains to be shown in robotics, where the generalization capabilities of the models are particularly critical due to the difficulty of collecting real-world robotic data. We argue that one of the keys to the success of such general robotic models lies with open-ended task-agnostic training, combined with high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks. The project's website and videos can be found at robotics-transformer.github.io

模型校准衡量预测的概率估计与真实性可能性之间的一致性。正确的模型校准对于高风险应用至关重要。不幸的是，现代深层神经网络的校准不佳，损害了可信度和可靠性。由于组织边界的自然不确定性，医疗图像分割尤其遭受了这种情况。这对他们的损失功能感到愤怒，这有利于多数级别的过度自信。我们用Domino（一种域感知的模型校准方法）解决了这些挑战，该方法利用了类标签之间的语义混淆性和分层相似性。我们的实验表明，在头部图像分割中，我们受多米诺骨牌校准的深神经网络优于非校准模型和最先进的形态学方法。我们的结果表明，与这些方法相比，我们的方法可以始终如一地实现更好的校准，更高的准确性和更快的推理时间，尤其是在稀有类别上。该性能归因于我们的域知觉正规化，以告知语义模型校准。这些发现表明，班级标签之间语义联系在建立深度学习模型的信心中的重要性。该框架有可能提高通用医学图像分割模型的可信度和可靠性。本文的代码可在以下网址获得：https：//github.com/lab-smile/domino。

ICECUBE是一种用于检测1 GEV和1 PEV之间大气和天体中微子的光学传感器的立方公斤阵列，该阵列已部署1.45 km至2.45 km的南极的冰盖表面以下1.45 km至2.45 km。来自ICE探测器的事件的分类和重建在ICeCube数据分析中起着核心作用。重建和分类事件是一个挑战，这是由于探测器的几何形状，不均匀的散射和冰中光的吸收，并且低于100 GEV的光，每个事件产生的信号光子数量相对较少。为了应对这一挑战，可以将ICECUBE事件表示为点云图形，并将图形神经网络（GNN）作为分类和重建方法。 GNN能够将中微子事件与宇宙射线背景区分开，对不同的中微子事件类型进行分类，并重建沉积的能量，方向和相互作用顶点。基于仿真，我们提供了1-100 GEV能量范围的比较与当前ICECUBE分析中使用的当前最新最大似然技术，包括已知系统不确定性的影响。对于中微子事件分类，与当前的IceCube方法相比，GNN以固定的假阳性速率（FPR）提高了信号效率的18％。另外，GNN在固定信号效率下将FPR的降低超过8（低于半百分比）。对于能源，方向和相互作用顶点的重建，与当前最大似然技术相比，分辨率平均提高了13％-20％。当在GPU上运行时，GNN能够以几乎是2.7 kHz的中位数ICECUBE触发速率的速率处理ICECUBE事件，这打开了在在线搜索瞬态事件中使用低能量中微子的可能性。

对于医疗保健提供者提供适当的患者护理的准确和详细说明，包括患者时​​间表中的药物变化，至关重要。医疗保健提供者或患者本身可能会引发患者药物的改变。用药更改采用多种形式，包括处方药和相关剂量修饰。这些更改提供了有关患者整体健康以及导致当前护理的理由的信息。然后，未来的护理可以基于患者的最终状态。这项工作探讨了从自由文本临床注释中自动提取药物变化信息。上下文药物事件数据集（CMED）是临床注释的语料库，其注释可以通过多种变化相关的属性来表征药物变化，包括更改的类型（启动，停止，增加等），更改，时间性，时间性，时间性，时间性，时间性，时间。改变可能性和否定。使用CMED，我们确定了临床文本中的药物提及，并提出了三个新型的基于BERT的新型基于BERT的系统，以解决注释的药物变化特征。我们证明，我们建议的体系结构改善了对CMED的初始工作改善药物变更分类的性能。我们确定了0.959 F1的高性能的药物提及，我们提出的系统将药物变化及其属性分类为0.827 F1。

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。

人工智能的最新趋势是将验证的模型用于语言和视觉任务，这些模型已经实现了非凡的表现，但也令人困惑。因此，以各种方式探索这些模型的能力对该领域至关重要。在本文中，我们探讨了模型的可靠性，在其中我们将可靠的模型定义为一个不仅可以实现强大的预测性能，而且在许多涉及不确定性（例如选择性预测，开放式设置识别）的决策任务上，在许多决策任务上表现出色，而且表现良好。强大的概括（例如，准确性和适当的评分规则，例如在分布数据集中和分发数据集上的对数可能性）和适应性（例如，主动学习，几乎没有射击不确定性）。我们设计了40个数据集的10种任务类型，以评估视觉和语言域上可靠性的不同方面。为了提高可靠性，我们分别开发了VIT-PLEX和T5-PLEX，分别针对视觉和语言方式扩展了大型模型。 PLEX极大地改善了跨可靠性任务的最先进，并简化了传统协议，因为它可以改善开箱即用的性能，并且不需要设计分数或为每个任务调整模型。我们演示了高达1B参数的模型尺寸的缩放效果，并预处理数据集大小最多4B示例。我们还展示了PLEX在具有挑战性的任务上的功能，包括零射门的开放式识别，主动学习和对话语言理解中的不确定性。

Transfer learning increasingly becomes an important tool in handling data scarcity often encountered in machine learning. In the application of high-throughput thickness as a downstream process of the high-throughput optimization of optoelectronic thin films with autonomous workflows, data scarcity occurs especially for new materials. To achieve high-throughput thickness characterization, we propose a machine learning model called thicknessML that predicts thickness from UV-Vis spectrophotometry input and an overarching transfer learning workflow. We demonstrate the transfer learning workflow from generic source domain of generic band-gapped materials to specific target domain of perovskite materials, where the target domain data only come from limited number (18) of refractive indices from literature. The target domain can be easily extended to other material classes with a few literature data. Defining thickness prediction accuracy to be within-10% deviation, thicknessML achieves 92.2% (with a deviation of 3.6%) accuracy with transfer learning compared to 81.8% (with a deviation of 3.6%) 11.7% without (lower mean and larger standard deviation). Experimental validation on six deposited perovskite films also corroborates the efficacy of the proposed workflow by yielding a 10.5% mean absolute percentage error (MAPE).