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.

Automatically identifying feature correspondences between multimodal images is facing enormous challenges because of the significant differences both in radiation and geometry. To address these problems, we propose a novel feature matching method, named R2FD2, that is robust to radiation and rotation differences.Our R2FD2 is conducted in two critical contributions, consisting of a repeatable feature detector and a rotation-invariant feature descriptor. In the first stage, a repeatable feature detector called the Multi-channel Auto-correlation of the Log-Gabor is presented for feature detection, which combines the multi-channel auto-correlation strategy with the Log-Gabor wavelets to detect interest points with high repeatability and uniform distribution. In the second stage, a rotation-invariant feature descriptor is constructed, named the Rotation-invariant Maximum index map of the Log-Gabor, which consists of two components: fast assignment of dominant orientation and construction of feature representation. In the process of fast assignment of dominant orientation, a Rotation-invariant Maximum Index Map is built to address rotation deformations. Then, the proposed RMLG incorporates the rotation-invariant RMIM with the spatial configuration of DAISY to depict a more discriminative feature representation, which improves RMLGs resistance to radiation and rotation variances.

尽管变形金刚及其变体构象体在语音识别方面表现出了有希望的表现，但参数化的属性在训练和推理过程中导致了很大的记忆成本。一些作品使用跨层重量分享来减少模型的参数。但是，不可避免的能力损失会损害模型性能。为了解决这个问题，本文提出了通过共享稀疏门控专家的参数效率构象异构体。具体而言，我们使用稀疏门控的专家（MOE）来扩展构型块的容量而不增加计算。然后，共享分组构象块的参数，以减少参数的数量。接下来，为了确保具有不同级别适应表示的灵活性的共享块，我们会单独设计MOE路由器和标准化。此外，我们使用知识蒸馏来进一步提高性能。实验结果表明，与全参数模型相比，所提出的模型用编码器的1/3来实现竞争性能。

最近，大多数手写的数学表达识别（HMER）方法采用编码器 - 编码器网络，该网络直接从具有注意机制的公式图像中直接预测标记序列。但是，此类方法可能无法准确读取具有复杂结构的公式或生成长的标记序列，因为由于写作样式或空间布局的差异很大，注意结果通常是不准确的。为了减轻此问题，我们为HMER提出了一个名为Counting-Aware-Aware网络（CAN）的非常规网络，该网络共同优化了两个任务：HMER和符号计数。具体而言，我们设计了一个弱监督的计数模块，该模块可以预测每个符号类的数量，而无需符号级别的位置注释，然后将其插入HMER的典型基于注意力的编码器模型。在基准数据集上进行的实验验证了关节优化和计数结果既有益于纠正编码器模型的预测误差，又可以始终如一地胜过最先进的方法。特别是，与HMER的编码器模型相比，提议的计数模块引起的额外时间成本是边缘的。源代码可从https://github.com/lbh1024/can获得。

尽管近年来从CT/MRI扫描中自动腹部多器官分割取得了很大进展，但由于缺乏各种临床方案的大规模基准，对模型的能力的全面评估受到阻碍。收集和标记3D医学数据的高成本的限制，迄今为止的大多数深度学习模型都由具有有限数量的感兴趣或样品器官的数据集驱动，这仍然限制了现代深层模型的力量提供各种方法的全面且公平的估计。为了减轻局限性，我们提出了AMO，这是一个大规模，多样的临床数据集，用于腹部器官分割。 AMOS提供了从多中心，多供应商，多模式，多相，多疾病患者收集的500 CT和100次MRI扫描，每个患者均具有15个腹部器官的体素级注释，提供了具有挑战性的例子，并提供了挑战性的例子和测试结果。在不同的目标和场景下研究健壮的分割算法。我们进一步基准了几种最先进的医疗细分模型，以评估此新挑战性数据集中现有方法的状态。我们已公开提供数据集，基准服务器和基线，并希望激发未来的研究。信息可以在https://amos22.grand-challenge.org上找到。

鸟眼视图（BEV）语义分割对于具有强大的空间表示能力的自动驾驶至关重要。由于空间间隙而从单眼图像中估算BEV语义图是一项挑战，因为这是隐含的，以实现均可实现透视到bev-bev的转换和分割。我们提出了一个新型的两阶段几何形状的基于GITNET的基于基于的转换框架，由（i）几何引导的预先对准和（ii）基于射线的变压器组成。在第一阶段，我们将BEV分割分解为透视图的图像分割和基于几何的基于几何映射，并通过将BEV语义标签投影到图像平面上，以明确的监督，以学习可见性吸引的特征和可学习的几何形状，以转化为BEV空间。其次，基于射线的变压器将预先一致的粗细BEV特征进一步变形，以考虑可见性知识。 Gitnet在具有挑战性的Nuscenes和Argoverse数据集上实现了领先的表现。

手写数学表达识别（HMER）是具有许多潜在应用的挑战性任务。 HMER的最新方法通过编码器架构实现了出色的性能。但是，这些方法符合“从一个字符到另一个字符”进行预测的范式，由于数学表达式或厌恶的手写的复杂结构，这不可避免地会产生预测错误。在本文中，我们为HMER提出了一种简单有效的方法，该方法是第一个将语法信息纳入编码器编码器网络的方法。具体而言，我们提出了一组语法规则，用于将每个表达式的乳胶标记序列转换为一个解析树。然后，我们将标记序列预测建模为具有深神经网络的树遍布过程。通过这种方式，提出的方法可以有效地描述表达式的语法上下文，从而减轻HMER的结构预测错误。在三个基准数据集上的实验表明，与先前的艺术相比，我们的方法实现了更好的识别性能。为了进一步验证我们方法的有效性，我们创建了一个大规模数据集，该数据集由从一万个作家中获取的100k手写数学表达图像组成。该工作的源代码，新数据集和预培训的模型将公开可用。

Graph neural network, as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. However, it has not been fully considered in graph neural network for heterogeneous graph which contains different types of nodes and links. The heterogeneity and rich semantic information bring great challenges for designing a graph neural network for heterogeneous graph. Recently, one of the most exciting advancements in deep learning is the attention mechanism, whose great potential has been well demonstrated in various areas. In this paper, we first propose a novel heterogeneous graph neural network based on the hierarchical attention, including node-level and semantic-level attentions. Specifically, the node-level attention aims to learn the importance between a node and its metapath based neighbors, while the semantic-level attention is able to learn the importance of different meta-paths. With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered. Then the proposed model can generate node embedding by aggregating features from meta-path based neighbors in a hierarchical manner. Extensive experimental results on three real-world heterogeneous graphs not only show the superior performance of our proposed model over the state-of-the-arts, but also demonstrate its potentially good interpretability for graph analysis.

Accurate determination of a small molecule candidate (ligand) binding pose in its target protein pocket is important for computer-aided drug discovery. Typical rigid-body docking methods ignore the pocket flexibility of protein, while the more accurate pose generation using molecular dynamics is hindered by slow protein dynamics. We develop a tiered tensor transform (3T) algorithm to rapidly generate diverse protein-ligand complex conformations for both pose and affinity estimation in drug screening, requiring neither machine learning training nor lengthy dynamics computation, while maintaining both coarse-grain-like coordinated protein dynamics and atomistic-level details of the complex pocket. The 3T conformation structures we generate are closer to experimental co-crystal structures than those generated by docking software, and more importantly achieve significantly higher accuracy in active ligand classification than traditional ensemble docking using hundreds of experimental protein conformations. 3T structure transformation is decoupled from the system physics, making future usage in other computational scientific domains possible.

For Prognostics and Health Management (PHM) of Lithium-ion (Li-ion) batteries, many models have been established to characterize their degradation process. The existing empirical or physical models can reveal important information regarding the degradation dynamics. However, there is no general and flexible methods to fuse the information represented by those models. Physics-Informed Neural Network (PINN) is an efficient tool to fuse empirical or physical dynamic models with data-driven models. To take full advantage of various information sources, we propose a model fusion scheme based on PINN. It is implemented by developing a semi-empirical semi-physical Partial Differential Equation (PDE) to model the degradation dynamics of Li-ion-batteries. When there is little prior knowledge about the dynamics, we leverage the data-driven Deep Hidden Physics Model (DeepHPM) to discover the underlying governing dynamic models. The uncovered dynamics information is then fused with that mined by the surrogate neural network in the PINN framework. Moreover, an uncertainty-based adaptive weighting method is employed to balance the multiple learning tasks when training the PINN. The proposed methods are verified on a public dataset of Li-ion Phosphate (LFP)/graphite batteries.