他们早期阶段的脑转移（BM）的检测可能对癌症患者的结果产生积极影响。我们以前开发了一种在T1加权对比度增强3D磁共振图像（T1C）中检测小BM（直径小于15mm）的框架，以帮助医学专家在这次时间敏感和高赌注任务中。该框架利用使用标记的T1C数据训练的专用卷积神经网络（CNN），其中基本真理BM分段由放射科医师提供。本研究旨在通过嘈杂的基于学生的自我培训策略推进框架，以利用未标记的T1C数据的大语料库（即，没有BM分段或检测的数据）。因此，工作（1）描述了学生和教师CNN架构，（2）提出数据和模型通知机制，（3）在框架的学习BM检测灵敏度中介绍了一种新的伪标记策略分解。最后，它描述了利用这些组件的半监督学习策略。我们通过2倍交叉验证使用标记为217和1247个未标记的T1C考试进行验证。仅使用标记的考试的框架产生了9.23个假阳性90％BM检测灵敏度;然而，使用所引入的学习策略的框架导致了相同的灵敏度水平的假检测（即8.44）减少了〜9％。此外，虽然利用75％和50％标记数据集的实验导致算法性能降级（分别为12.19和13.89误），但随着基于嘈杂的学生的培训策略（分别为10.79和12.37误报），影响不太明显。

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.

Likelihood-based deep generative models have recently been shown to exhibit pathological behaviour under the manifold hypothesis as a consequence of using high-dimensional densities to model data with low-dimensional structure. In this paper we propose two methodologies aimed at addressing this problem. Both are based on adding Gaussian noise to the data to remove the dimensionality mismatch during training, and both provide a denoising mechanism whose goal is to sample from the model as though no noise had been added to the data. Our first approach is based on Tweedie's formula, and the second on models which take the variance of added noise as a conditional input. We show that surprisingly, while well motivated, these approaches only sporadically improve performance over not adding noise, and that other methods of addressing the dimensionality mismatch are more empirically adequate.

Machine-learning models are increasingly used to predict properties of atoms in chemical systems. There have been major advances in developing descriptors and regression frameworks for this task, typically starting from (relatively) small sets of quantum-mechanical reference data. Larger datasets of this kind are becoming available, but remain expensive to generate. Here we demonstrate the use of a large dataset that we have "synthetically" labelled with per-atom energies from an existing ML potential model. The cheapness of this process, compared to the quantum-mechanical ground truth, allows us to generate millions of datapoints, in turn enabling rapid experimentation with atomistic ML models from the small- to the large-data regime. This approach allows us here to compare regression frameworks in depth, and to explore visualisation based on learned representations. We also show that learning synthetic data labels can be a useful pre-training task for subsequent fine-tuning on small datasets. In the future, we expect that our open-sourced dataset, and similar ones, will be useful in rapidly exploring deep-learning models in the limit of abundant chemical data.

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.

机器学习潜力是分子模拟的重要工具，但是由于缺乏高质量数据集来训练它们的发展，它们的开发阻碍了它们。我们描述了Spice数据集，这是一种新的量子化学数据集，用于训练与模拟与蛋白质相互作用的药物样的小分子相关的潜在。它包含超过110万个小分子，二聚体，二肽和溶剂化氨基酸的构象。它包括15个元素，带电和未充电的分子以及广泛的共价和非共价相互作用。它提供了在{\ omega} b97m-d3（bj）/def2-tzVPPD理论水平以及其他有用的数量（例如多极矩和键阶）上计算出的力和能量。我们在其上训练一组机器学习潜力，并证明它们可以在化学空间的广泛区域中实现化学精度。它可以作为创建可转移的，准备使用潜在功能用于分子模拟的宝贵资源。

神经网络修剪可以有效地用于压缩自动语音识别（ASR）模型。但是，在多语言ASR中，执行语言不足的修剪可能会导致某些语言的严重性能降解，因为语言 - 敏捷的修剪口罩可能不符合所有语言，并丢弃了重要的语言特定参数。在这项工作中，我们提出了ASR路径，这是一种稀疏的多语言ASR模型，该模型激活了特定语言的子网络（“路径”），从而明确地学习了每种语言的参数。通过重叠的子网络，共享参数还可以通过联合多语言培训来实现较低资源语言的知识传输。我们提出了一种新型算法来学习ASR途径，并通过流式RNN-T模型评估了4种语言的建议方法。我们提出的ASR途径的表现都优于密集模型（平均-5.0％）和语言不足的修剪模型（平均-21.4％），并且与单语稀疏模型相比，低资源语言的性能更好。

3D多对象跟踪（MOT）是自动驾驶汽车的关键问题，需要在动态环境中执行信息良好的运动计划。特别是对于密集的占领场景，将现有曲目与新检测相关联仍然具有挑战性，因为现有系统倾向于省略关键的上下文信息。我们提出的解决方案InterTrack引入了3D MOT的相互作用变压器，以生成数据关联的区分对象表示。我们为每个轨道和检测提取状态和形状特征，并通过注意力有效地汇总全局信息。然后，我们对每个轨道/检测功能对进行学习的回归以估计亲和力，并使用强大的两阶段数据关联和轨道管理方法来生成最终轨道。我们在Nuscenes 3D MOT基准上验证了我们的方法，在那里我们观察到了显着的改进，尤其是在物理大小和聚类对象的类别上。从提交开始时，InterTrack在使用CenterPoint检测的方法中排名第1位AMOTA。

胸部X射线（CXR）成像的作用，由于更具成本效益，可广泛可用，并且与CT相比具有更快的获取时间，在Covid-19-19-19大流行期间已经演变。为了提高CXR成像的诊断性能，越来越多的研究研究了监督深度学习方法是否可以提供额外的支持。但是，有监督的方法依靠大量标记的放射学图像，这是一项耗时且复杂的程序，需要专家临床医生的输入。由于COVID-19患者数据的相对稀缺性和昂贵的标签过程，因此，自我监督的学习方法已获得动力，并已提出与完全监督的学习方法相当的结果。在这项工作中，我们研究了从CXR图像诊断Covid-19疾病的背景下，自我监督学习的有效性。我们提出了一个多功能视觉变压器（VIT）引导体系结构，在该体系结构中我们部署了交叉注意机制，以从原始CXR图像和相应增强的局部CXR图像中学习信息。我们通过利用基于局部阶段的增强的CXR图像来进一步改善基线自学学习模型的性能。通过使用10 \％标记的CXR扫描，该模型可实现91.10 \％和96.21 \％的总体精度，总计为35,483 CXR的健康（8,851）（8,851），常规肺炎（6,045）和COVID-19（18,159）（18,159）（18,159）（18,159）（18,159）（18,159）扫描对最新技术的显着改善。代码可用https://github.com/endiqq/multi-feature-vit

最近，注意机制已成功应用于基于神经网络的说话者验证系统。将挤压和兴奋的块纳入卷积神经网络中的表现出色。但是，它使用全球平均池（GAP）简单地沿时间和频率维度平均功能，这无法在功能地图中保留足够的扬声器信息。在这项研究中，我们表明GAP是时间频域在数学上仅使用频率分解中最低频率分量的特殊情况。为了增强扬声器信息提取能力，我们建议利用多频信息，并设计两个新颖的有效注意模块，称为单频率单通道（SFSC）注意模块和多频单通道（MFSC）注意模块。提出的注意模块可以根据DCT有效地从多个频率组件中捕获更多扬声器信息。我们在Voxceleb数据集上进行了全面的实验，并对第148个UTD法医语料库进行了探测评估。实验结果表明，我们提出的SFSC和MFSC注意模块可以有效地产生更具歧视性的扬声器表示，并且优于RESNET34-SE和ECAPA-TDNN系统，而EER降低了20.9％和20.2％，而无需添加额外的网络参数。