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.

Understanding customer feedback is becoming a necessity for companies to identify problems and improve their products and services. Text classification and sentiment analysis can play a major role in analyzing this data by using a variety of machine and deep learning approaches. In this work, different transformer-based models are utilized to explore how efficient these models are when working with a German customer feedback dataset. In addition, these pre-trained models are further analyzed to determine if adapting them to a specific domain using unlabeled data can yield better results than off-the-shelf pre-trained models. To evaluate the models, two downstream tasks from the GermEval 2017 are considered. The experimental results show that transformer-based models can reach significant improvements compared to a fastText baseline and outperform the published scores and previous models. For the subtask Relevance Classification, the best models achieve a micro-averaged $F1$-Score of 96.1 % on the first test set and 95.9 % on the second one, and a score of 85.1 % and 85.3 % for the subtask Polarity Classification.

Purpose: Hard-to-interpret Black-box Machine Learning (ML) were often used for early Alzheimer's Disease (AD) detection. Methods: To interpret eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM) black-box models a workflow based on Shapley values was developed. All models were trained on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and evaluated for an independent ADNI test set, as well as the external Australian Imaging and Lifestyle flagship study of Ageing (AIBL), and Open Access Series of Imaging Studies (OASIS) datasets. Shapley values were compared to intuitively interpretable Decision Trees (DTs), and Logistic Regression (LR), as well as natural and permutation feature importances. To avoid the reduction of the explanation validity caused by correlated features, forward selection and aspect consolidation were implemented. Results: Some black-box models outperformed DTs and LR. The forward-selected features correspond to brain areas previously associated with AD. Shapley values identified biologically plausible associations with moderate to strong correlations with feature importances. The most important RF features to predict AD conversion were the volume of the amygdalae, and a cognitive test score. Good cognitive test performances and large brain volumes decreased the AD risk. The models trained using cognitive test scores significantly outperformed brain volumetric models ($p<0.05$). Cognitive Normal (CN) vs. AD models were successfully transferred to external datasets. Conclusion: In comparison to previous work, improved performances for ADNI and AIBL were achieved for CN vs. Mild Cognitive Impairment (MCI) classification using brain volumes. The Shapley values and the feature importances showed moderate to strong correlations.

糖尿病足溃疡是糖尿病脚对病变的常见表现，是一种作为糖尿病糖尿病的长期并发症的综合征。伴随着神经病变和血管损伤促进因缺血而收购压力损伤和组织死亡。受影响的区域易于感染，阻碍治疗进展。手头的研究调查了作为糖尿病足溃疡攻击（DFUC）2021的一部分进行的感染和缺血性的方法。有效的家庭的不同模型用于合奏。应用培训数据的扩展策略，涉及未标记的图像伪标记，并通过PIX2PIXHD广泛地产生合成图像，以应对严重的类别不平衡。由此产生的扩展训练数据集具有3.68美元的基线大小，并显示了1：3 $ 1：3 $的合成图像比率。比较了在基线和扩展训练数据集上培训的模型和合奏的性能。合成图像具有广泛的品质品种。结果表明，型号在扩展训练数据集上培训以及它们的集合受益于大型扩展。罕见课程的F1分数得到了出色的提升，而常见类别的人则不受伤害或适度促进。批判性讨论具体化益处并确定限制，建议改进。该工作得出结论，各个模型的分类性能以及集合的分类性能可以利用合成图像提升。特别是对罕见课程的表现尤其效益。

Computational fluid dynamics (CFD) is a valuable asset for patient-specific cardiovascular-disease diagnosis and prognosis, but its high computational demands hamper its adoption in practice. Machine-learning methods that estimate blood flow in individual patients could accelerate or replace CFD simulation to overcome these limitations. In this work, we consider the estimation of vector-valued quantities on the wall of three-dimensional geometric artery models. We employ group-equivariant graph convolution in an end-to-end SE(3)-equivariant neural network that operates directly on triangular surface meshes and makes efficient use of training data. We run experiments on a large dataset of synthetic coronary arteries and find that our method estimates directional wall shear stress (WSS) with an approximation error of 7.6% and normalised mean absolute error (NMAE) of 0.4% while up to two orders of magnitude faster than CFD. Furthermore, we show that our method is powerful enough to accurately predict transient, vector-valued WSS over the cardiac cycle while conditioned on a range of different inflow boundary conditions. These results demonstrate the potential of our proposed method as a plugin replacement for CFD in the personalised prediction of hemodynamic vector and scalar fields.

个性化的3D血管模型对于心血管疾病患者的诊断，预后和治疗计划很有价值。传统上，这样的模型是用明确表示（例如网格和体素掩码）构建的，或隐式表示，例如径向基函数或原子（管状）形状。在这里，我们建议在可区分的隐式神经表示（INR）中以其签名距离函数（SDF）的零级集表示表面。这使我们能够用隐性，连续，轻巧且易于与深度学习算法集成的表示复杂的血管结构对复杂的血管结构进行建模。我们在这里通过三个实际示例证明了这种方法的潜力。首先，我们从CT图像中获得了腹主动脉瘤（AAA）的精确和水密表面，并显示出从表面上的200点出现的可靠拟合。其次，我们同时将嵌套的容器壁贴在一个没有交叉点的单个INR中。第三，我们展示了如何将3D模型的单个动脉模型平滑地混合到单个水密表面。我们的结果表明，INR是一种灵活的表示，具有微小互动注释和操纵复杂血管结构的潜力。

深度学习方法正在成为高能量物理（HEP）中数据分析的首选方法。尽管如此，大多数以物理启发的现代体系结构在计算上效率低下，缺乏解释性。JET标记算法尤其如此，考虑到现代粒子探测器产生的大量数据，计算效率至关重要。在这项工作中，我们为喷气式代表介绍了一个新颖，多功能和透明的框架。Lorentz Group Boosts不变，这在喷气标记基准测试基准方面具有很高的精度，同时比其他现代方法更快地训练和评估了训练和评估。

在查询图像中检索与感兴趣的对象（OOI）在语义上相似的对象具有许多实际用例。一些示例包括修复失败，例如虚假的负面因素/阳性模型或减轻数据集中的类不平衡。有针对性的选择任务需要从大规模的未标记数据池中找到相关数据。在此规模上进行手动开采是不可行的。此外，OOI通常很小，占据图像区域的1％不到1％，被遮挡，并且在混乱的场景中与许多语义上不同的物体共存。现有的语义图像检索方法通常集中在较大尺寸的地理地标的采矿和/或需要额外的标记数据，例如带有相似对象的图像/图像对，用于带有通用对象的挖掘图像。我们在DNN功能空间中提出了一个匹配算法的快速稳固的模板，该模板从一个大的未标记数据池中检索了对象级的语义相似图像。我们将查询图像中OOI周围的区域投射到DNN功能空间以用作模板。这使我们的方法能够专注于OOI的语义，而无需额外的标记数据。在自主驾驶的背景下，我们通过将对象探测器的故障案例作为OOI评估我们的系统进行靶向选择。我们证明了其在具有2.2m图像的大型未标记数据集上的功效，并在采矿中显示出对具有小型OOI的图像的高回忆。我们将我们的方法与众所周知的语义图像检索方法进行比较，该方法也不需要额外的标记数据。最后，我们证明我们的方法是灵活的，并以一种或多种语义上不同的同时发生的OOI无缝地检索图像。

由于它们对运动模糊和在弱光和高动态范围条件下的高度鲁棒性的韧性，事件摄像机有望成为对未来火星直升机任务的基于视觉探索的传感器。但是，现有的基于事件的视觉惯性进程（VIO）算法要么患有高跟踪误差，要么是脆弱的，因为它们无法应对由于无法预料的跟踪损失或其他效果而导致的显着深度不确定性。在这项工作中，我们介绍了EKLT-VIO，该工作通过将基于事件的最新前端与基于过滤器的后端相结合来解决这两种限制。这使得不确定性的准确和强大，超过了基于事件和基于框架的VIO算法在挑战性基准上的算法32％。此外，我们在悬停的条件（胜过现有事件的方法）以及新近收集的类似火星和高动态范围的新序列中表现出准确的性能，而现有的基于框架的方法失败了。在此过程中，我们表明基于事件的VIO是基于视觉的火星探索的前进道路。

颈动脉壳壁厚测量是监测动脉粥样硬化患者的重要步骤。这需要精确分割血管壁，即动脉的内腔和外壁之间的区域，在黑血磁共振（MR）图像中。对于语义分割的常用卷积神经网络（CNNS）是本任务的次优，因为它们的使用不保证连续的环形分割。相反，在这项工作中，我们将船舶壁分段作为极坐标系中的多任务回归问题。对于每个轴向图像切片中的每种颈动脉，我们的目的是同时发现两个非交叉的嵌套轮廓，在一起叠加血管壁。应用于此问题的CNNS使电感偏压能够保证环形血管壁。此外，我们确定了一个特定于问题的培训数据增强技术，其大大影响了分割性能。我们将我们的方法应用于内部和外部颈动脉壁的分割，并在公共挑战中实现排名级定量结果，即血管墙壁的中值骰子相似系数为0.813，中位Hausdorff距离为0.552 mm和0.776 mm对于内腔和外墙。此外，我们展示了如何通过传统的语义分割方法来改善方法。这些结果表明，可以高精度地自动获得颈动脉壁的解剖学似合子分割是可行的。