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.

Despite high global prevalence of hepatic steatosis, no automated diagnostics demonstrated generalizability in detecting steatosis on multiple international datasets. Traditionally, hepatic steatosis detection relies on clinicians selecting the region of interest (ROI) on computed tomography (CT) to measure liver attenuation. ROI selection demands time and expertise, and therefore is not routinely performed in populations. To automate the process, we validated an existing artificial intelligence (AI) system for 3D liver segmentation and used it to purpose a novel method: AI-ROI, which could automatically select the ROI for attenuation measurements. AI segmentation and AI-ROI method were evaluated on 1,014 non-contrast enhanced chest CT images from eight international datasets: LIDC-IDRI, NSCLC-Lung1, RIDER, VESSEL12, RICORD-1A, RICORD-1B, COVID-19-Italy, and COVID-19-China. AI segmentation achieved a mean dice coefficient of 0.957. Attenuations measured by AI-ROI showed no significant differences (p = 0.545) and a reduction of 71% time compared to expert measurements. The area under the curve (AUC) of the steatosis classification of AI-ROI is 0.921 (95% CI: 0.883 - 0.959). If performed as a routine screening method, our AI protocol could potentially allow early non-invasive, non-pharmacological preventative interventions for hepatic steatosis. 1,014 expert-annotated liver segmentations of patients with hepatic steatosis annotations can be downloaded here: https://drive.google.com/drive/folders/1-g_zJeAaZXYXGqL1OeF6pUjr6KB0igJX.

逆合合成是一种将分子转化为潜在反应物的过程，因此鉴定了合成途径。我们提出了一个新颖的生成框架，称为$ \ mathsf {g^2retro} $，用于一步回曲预测。 $ \ mathsf {g^2retro} $模仿合成反应的反向逻辑，也就是说，首先预测反应中心以将靶分子转换为名为合成的片段，然后将合成剂转化为反应剂，然后按照先前的基于半电压的方法转换为反应剂。在预测反应中心时，$ \ mathsf {g^2retro} $定义了一组全面的反应中心类型，并通过考虑多个反应中心候选者来实现预测反应的多样性。在完成合成子时，$ \ mathsf {g^2retro} $部署了一系列子结构附件，以将合成物转换为反应物，该反应物利用了要完成的合成结构的最新结构的整体视图，以及所有所涉及的合成物和所有合成的结构产品结构。在这里，我们证明$ \ mathsf {g^2retro} $能够更好地对基准数据集中最可能的反应物进行优先级，而不是最先进的方法，并且发现了不包括在该方法中基准数据集。

基于生成模型的图像无损压缩算法在改善压缩比方面取得了巨大的成功。但是，即使使用最先进的AI加速芯片，它们中大多数的吞吐量也小于1 Mb/s，从而阻止了它们的大多数现实应用应用，通常需要100 MB/s。在本文中，我们提出了PILC，这是一种端到端图像无损压缩框架，使用单个NVIDIA TESLA V100 GPU实现200 Mb/s的压缩和减压，比以前最有效的速度快10倍。为了获得此结果，我们首先开发了一个AI编解码器，该AI编解码器结合了自动回归模型和VQ-VAE，在轻质设置中性能很好，然后我们设计了一个低复杂性熵编码器，可与我们的编解码器配合使用。实验表明，在多个数据集中，我们的框架压缩比PNG高30％。我们认为，这是将AI压缩推向商业用途的重要步骤。

分子机器学习的最新进展，特别是深度神经网络，如图形神经网络（GNNS），用于预测结构活动关系（SAR）在计算机辅助药物发现中表达了巨大的潜力。然而，这种深神经网络的适用性受到大量培训数据的限制。为了应对目标任务的有限培训数据，最近已采用对SAR建模的转移学习，从而利用相关任务数据的信息。在这项工作中，与最流行的基于参数的转移学习相比，诸如预先估计的基于流行的传输学习，我们开发了新颖的深度传输学习方法TAC和TAC-FC来利用源域数据并将有用信息传送到目标域。 TAC学习生成可以从一个域概括到另一个域的有效分子特征，并提高目标域中的分类性能。另外，TAC-FC通过掺入新的组分来选择性地学习特征和化合物方识的可转移性来延伸TAC。我们使用来自Pubchem的生物测定筛选数据，并确定了120对生物测定，使得与其无活性化合物相比，每对的活性化合物彼此更类似。总的来说，TAC实现了平均Roc-AUC的最佳性能为0.801;与最佳基线FCN-DMPNA（DT）相比，它显着提高了83％的目标任务的83％的目标任务，平均任务明智的性能提高为7.102％。我们的实验清楚地表明TAC在大量目标任务中对所有基线实现了重大改进。此外，尽管与TAC相比，TAC-FC略微较差的ROC-AUC（0.798 VS 0.801），但与其他方法相比，TAC-FC仍然在PR-AUC和F1方面实现了更好的性能。

虽然深入学习在固定的大型数据集中取得了重大进展，但它通常遇到关于在开放世界场景，过度参数化和过度拟合小型样本中检测到未知/看不见的课程的挑战。由于生物系统可以很好地克服上述困难，因此个体从集体生物中继承了一个先天基因，这些生物已经进化了数十亿多年，然后通过少数例子学习新技能。灵感来自这一点，我们提出了一个实用的集体个人范式，其中进化（可扩展）网络在顺序任务上培训，然后识别现实世界中的未知课程。此外，提出了学习者，即用于学习目标模型的初始化规则的基因，从集体模型继承了元知识，并在目标任务中重建轻量级各个模型。特别地，根据梯度信息，提出了一种新的标准来发现集体模型中的学习者。最后，只有在目标学习任务上的少量样本才接受培训。我们在广泛的实证研究和理论分析中展示了我们方法的有效性。

在药物发现中，分子优化是在所需药物性质方面将药物候选改变为更好的阶梯。随着近期人工智能的进展，传统上的体外过程越来越促进了Silico方法。我们以硅方法提出了一种创新的，以通过深生成模型制定分子并制定问题，以便产生优化的分子图。我们的生成模型遵循基于片段的药物设计的关键思想，并通过修改其小碎片来优化分子。我们的模型了解如何识别待优化的碎片以及如何通过学习具有良好和不良性质的分子的差异来修改此类碎片。在优化新分子时，我们的模型将学习信号应用于在片段的预测位置解码优化的片段。我们还将多个这样的模型构造成管道，使得管道中的每个模型能够优化一个片段，因此整个流水线能够在需要时改变多个分子片段。我们将我们的模型与基准数据集的其他最先进的方法进行比较，并证明我们的方法在中等分子相似度约束下具有超过80％的性质改善，在高分子相似度约束下具有超过80％的财产改善。 。

下一篮子推荐考虑将一组项目推荐到用户将作为一个整体购买的下一个篮子。在本文中，我们为下一个篮子推荐开发了一种具有偏好，普及和转换（M2）的新颖混合模型。该方法在下一个篮子生成过程中模拟了三个重要因素：1）用户在项目中的“全球偏好”，2）项目的“全球受欢迎者和3”的过渡模式。与现有的基于内部网络的方法不同，M2不使用复杂的网络来模拟项目之间的转换，或为用户生成嵌入品。相反，它具有基于简单的编码器解码器的方法（ED-Trans），以更好地模拟项目之间的转换模式。我们将M2与不同组合的组合进行了比较，其中有5个现有的下一篮子推荐方法在4个公共基准数据集上推荐第一个，第二和第三篮子。我们的实验结果表明，M2显着优于所有任务中所有数据集的最先进的方法，提高了高达22.1％。此外，我们的消融研究表明，在推荐性能方面，ED-Trans比经常性神经网络更有效。我们还对下一个篮子推荐评估进行了彻底讨论了各种实验协议和评估指标。

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.

In this work, we focus on instance-level open vocabulary segmentation, intending to expand a segmenter for instance-wise novel categories without mask annotations. We investigate a simple yet effective framework with the help of image captions, focusing on exploiting thousands of object nouns in captions to discover instances of novel classes. Rather than adopting pretrained caption models or using massive caption datasets with complex pipelines, we propose an end-to-end solution from two aspects: caption grounding and caption generation. In particular, we devise a joint Caption Grounding and Generation (CGG) framework based on a Mask Transformer baseline. The framework has a novel grounding loss that performs explicit and implicit multi-modal feature alignments. We further design a lightweight caption generation head to allow for additional caption supervision. We find that grounding and generation complement each other, significantly enhancing the segmentation performance for novel categories. We conduct extensive experiments on the COCO dataset with two settings: Open Vocabulary Instance Segmentation (OVIS) and Open Set Panoptic Segmentation (OSPS). The results demonstrate the superiority of our CGG framework over previous OVIS methods, achieving a large improvement of 6.8% mAP on novel classes without extra caption data. Our method also achieves over 15% PQ improvements for novel classes on the OSPS benchmark under various settings.