成像生物标志物提供了一种无创的方法来预测治疗前免疫疗法的反应。在这项工作中，我们提出了一种从卷积神经网络（CNN）计算出的新型深度放射素特征（DRF），该特征捕获了与免疫细胞标记和整体生存有关的肿瘤特征。我们的研究使用四个MRI序列（T1加权，T1加权后对比，T2加权和FLAIR），并具有151例脑肿瘤患者的相应免疫细胞标记。该方法通过在MRI扫描的标记肿瘤区域内聚集了预训练的3D-CNN的激活图，从而提取了180个DRF。这些功能提供了编码组织异质性的区域纹理的紧凑而有力的表示。进行了一组全面的实验，以评估所提出的DRF和免疫细胞标记之间的关系，并衡量它们与整体生存的关联。结果表明，DRF和各种标记之间存在很高的相关性，以及根据这些标记分组的患者之间的显着差异。此外，将DRF，临床特征和免疫细胞标记组合为随机森林分类器的输入有助于区分短期和长期生存结果，AUC为72 \％，P = 2.36 $ \ times $ 10 $^{ - 5} $。这些结果证明了拟议的DRF作为非侵入性生物标志物在预测脑肿瘤患者的治疗反应中的有用性。

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.

通常通过过去的选择来告知机器学习中的评估，例如要使用哪些数据集或指标。该标准化可以使用排行榜对平等基础进行比较，但是随着出现更好的替代方案，评估选择变得不佳。这个问题在自然语言生成中尤其相关，该语言需要不断改善的数据集，指标和人类评估以提出确定性的主张。为了使遵循最佳模型评估实践更加容易，我们介绍了GEMV2。新版本的一代，评估和指标基准为数据集，模型和指标开发人员提供了模块化基础架构，以使彼此受益。GEMV2支持40种记录的数据集中51种语言。所有数据集的模型都可以在线评估，我们的交互式数据卡创建和渲染工具使得在Living Benchmark中添加新数据集变得更加容易。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data. However, most current SSL techniques in the medical field have been designed for either 2D images or 3D volumes. In practice, this restricts the capability to fully leverage unlabeled data from numerous sources, which may include both 2D and 3D data. Additionally, the use of these pre-trained networks is constrained to downstream tasks with compatible data dimensions. In this paper, we propose a novel framework for unsupervised joint learning on 2D and 3D data modalities. Given a set of 2D images or 2D slices extracted from 3D volumes, we construct an SSL task based on a 2D contrastive clustering problem for distinct classes. The 3D volumes are exploited by computing vectored embedding at each slice and then assembling a holistic feature through deformable self-attention mechanisms in Transformer, allowing incorporating long-range dependencies between slices inside 3D volumes. These holistic features are further utilized to define a novel 3D clustering agreement-based SSL task and masking embedding prediction inspired by pre-trained language models. Experiments on downstream tasks, such as 3D brain segmentation, lung nodule detection, 3D heart structures segmentation, and abnormal chest X-ray detection, demonstrate the effectiveness of our joint 2D and 3D SSL approach. We improve plain 2D Deep-ClusterV2 and SwAV by a significant margin and also surpass various modern 2D and 3D SSL approaches.

Measuring growth rates of apple fruitlets is important because it allows apple growers to determine when to apply chemical thinners to their crops to optimize yield. The current practice of obtaining growth rates involves using calipers to record sizes of fruitlets across multiple days. Due to the number of fruitlets needed to be sized, this method is laborious, time-consuming, and prone to human error. In this paper, we present a computer vision approach to measure the sizes and growth rates of apple fruitlets. With images collected by a hand-held stereo camera, our system detects, segments, and fits ellipses to fruitlets to measure their diameters. To measure growth rates, we utilize an Attentional Graph Neural Network to associate fruitlets across different days. We provide quantitative results on data collected in an apple orchard, and demonstrate that our system is able to predict abscise rates within 3% of the current method with a 7 times improvement in speed, while requiring significantly less manual effort. Moreover, we provide results on images captured by a robotic system in the field, and discuss the next steps to make the process fully autonomous.

Deep learning has been a popular topic and has achieved success in many areas. It has drawn the attention of researchers and machine learning practitioners alike, with developed models deployed to a variety of settings. Along with its achievements, research has shown that deep learning models are vulnerable to adversarial attacks. This finding brought about a new direction in research, whereby algorithms were developed to attack and defend vulnerable networks. Our interest is in understanding how these attacks effect change on the intermediate representations of deep learning models. We present a method for measuring and analyzing the deviations in representations induced by adversarial attacks, progressively across a selected set of layers. Experiments are conducted using an assortment of attack algorithms, on the CIFAR-10 dataset, with plots created to visualize the impact of adversarial attacks across different layers in a network.

DeepMind的游戏理论与多代理团队研究多学科学习的几个方面，从计算近似值到游戏理论中的基本概念，再到在富裕的空间环境中模拟社会困境，并在困难的团队协调任务中培训3-D类人动物。我们小组的一个签名目的是使用DeepMind在DeepMind中提供的资源和专业知识，以深入强化学习来探索复杂环境中的多代理系统，并使用这些基准来提高我们的理解。在这里，我们总结了我们团队的最新工作，并提出了一种分类法，我们认为这重点介绍了多代理研究中许多重要的开放挑战。

已经开发了各种方法来结合多组结果的推理，以在集合和共识聚类文献中进行无监督的聚类。从几个候选聚类模型中的一个“最佳”模型报告结果的方法通常忽略了由模型选择产生的不确定性，并且导致对所选择的特定模型和参数敏感的推论，以及制作的假设，尤其是在小样本中所做的假设。尺寸或小簇尺寸。贝叶斯模型平均（BMA）是一种在多种模型中结合结果的流行方法，这些模型在这种情况下提供了一些有吸引力的好处，包括对组合集群结构的概率解释和基于模型的不确定性的量化。在这项工作中，我们介绍了ClusterBMA，该方法可以通过多种无监督聚类算法进行加权模型平均。我们将聚类内部验证标准的组合用作后验模型概率的新近似值，以加权每个模型的结果。从代表跨模型的聚类溶液的加权平均值的组合后相似性矩阵，我们应用对称的单纯形矩阵分解来计算最终的概率群集分配。此方法在随附的R软件包中实现。我们通过案例研究探索这种方法的性能，该案例研究旨在根据脑电图（EEG）数据识别个体的概率簇。我们还使用仿真数据集探索所提出的技术识别稳健的集成簇具有不同级别的集成簇，并在子组之间的分离水平变化，并且模型之间的簇数量变化。

心血管疾病是全球死亡的主要原因，是一种与年龄有关的疾病。了解衰老期间心脏的形态和功能变化是一个关键的科学问题，其答案将有助于我们定义心血管疾病的重要危险因素并监测疾病进展。在这项工作中，我们提出了一种新型的条件生成模型，以描述衰老过程中心脏3D解剖学的变化。提出的模型是灵活的，可以将多个临床因素（例如年龄，性别）整合到生成过程中。我们在心脏解剖学的大规模横截面数据集上训练该模型，并在横截面和纵向数据集上进行评估。该模型在预测衰老心脏的纵向演化和对其数据分布进行建模方面表现出了出色的表现。