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.

Surgery is the only viable treatment for cataract patients with visual acuity (VA) impairment. Clinically, to assess the necessity of cataract surgery, accurately predicting postoperative VA before surgery by analyzing multi-view optical coherence tomography (OCT) images is crucially needed. Unfortunately, due to complicated fundus conditions, determining postoperative VA remains difficult for medical experts. Deep learning methods for this problem were developed in recent years. Although effective, these methods still face several issues, such as not efficiently exploring potential relations between multi-view OCT images, neglecting the key role of clinical prior knowledge (e.g., preoperative VA value), and using only regression-based metrics which are lacking reference. In this paper, we propose a novel Cross-token Transformer Network (CTT-Net) for postoperative VA prediction by analyzing both the multi-view OCT images and preoperative VA. To effectively fuse multi-view features of OCT images, we develop cross-token attention that could restrict redundant/unnecessary attention flow. Further, we utilize the preoperative VA value to provide more information for postoperative VA prediction and facilitate fusion between views. Moreover, we design an auxiliary classification loss to improve model performance and assess VA recovery more sufficiently, avoiding the limitation by only using the regression metrics. To evaluate CTT-Net, we build a multi-view OCT image dataset collected from our collaborative hospital. A set of extensive experiments validate the effectiveness of our model compared to existing methods in various metrics. Code is available at: https://github.com/wjh892521292/Cataract OCT.

Positive-Unlabeled (PU) learning tries to learn binary classifiers from a few labeled positive examples with many unlabeled ones. Compared with ordinary semi-supervised learning, this task is much more challenging due to the absence of any known negative labels. While existing cost-sensitive-based methods have achieved state-of-the-art performances, they explicitly minimize the risk of classifying unlabeled data as negative samples, which might result in a negative-prediction preference of the classifier. To alleviate this issue, we resort to a label distribution perspective for PU learning in this paper. Noticing that the label distribution of unlabeled data is fixed when the class prior is known, it can be naturally used as learning supervision for the model. Motivated by this, we propose to pursue the label distribution consistency between predicted and ground-truth label distributions, which is formulated by aligning their expectations. Moreover, we further adopt the entropy minimization and Mixup regularization to avoid the trivial solution of the label distribution consistency on unlabeled data and mitigate the consequent confirmation bias. Experiments on three benchmark datasets validate the effectiveness of the proposed method.Code available at: https://github.com/Ray-rui/Dist-PU-Positive-Unlabeled-Learning-from-a-Label-Distribution-Perspective.

With the growth of high-dimensional sparse data in web-scale recommender systems, the computational cost to learn high-order feature interaction in CTR prediction task largely increases, which limits the use of high-order interaction models in real industrial applications. Some recent knowledge distillation based methods transfer knowledge from complex teacher models to shallow student models for accelerating the online model inference. However, they suffer from the degradation of model accuracy in knowledge distillation process. It is challenging to balance the efficiency and effectiveness of the shallow student models. To address this problem, we propose a Directed Acyclic Graph Factorization Machine (KD-DAGFM) to learn the high-order feature interactions from existing complex interaction models for CTR prediction via Knowledge Distillation. The proposed lightweight student model DAGFM can learn arbitrary explicit feature interactions from teacher networks, which achieves approximately lossless performance and is proved by a dynamic programming algorithm. Besides, an improved general model KD-DAGFM+ is shown to be effective in distilling both explicit and implicit feature interactions from any complex teacher model. Extensive experiments are conducted on four real-world datasets, including a large-scale industrial dataset from WeChat platform with billions of feature dimensions. KD-DAGFM achieves the best performance with less than 21.5% FLOPs of the state-of-the-art method on both online and offline experiments, showing the superiority of DAGFM to deal with the industrial scale data in CTR prediction task. Our implementation code is available at: https://github.com/RUCAIBox/DAGFM.

Federated learning (FL) enables the building of robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data. We created NVIDIA FLARE as an open-source software development kit (SDK) to make it easier for data scientists to use FL in their research and real-world applications. The SDK includes solutions for state-of-the-art FL algorithms and federated machine learning approaches, which facilitate building workflows for distributed learning across enterprises and enable platform developers to create a secure, privacy-preserving offering for multiparty collaboration utilizing homomorphic encryption or differential privacy. The SDK is a lightweight, flexible, and scalable Python package, and allows researchers to bring their data science workflows implemented in any training libraries (PyTorch, TensorFlow, XGBoost, or even NumPy) and apply them in real-world FL settings. This paper introduces the key design principles of FLARE and illustrates some use cases (e.g., COVID analysis) with customizable FL workflows that implement different privacy-preserving algorithms. Code is available at https://github.com/NVIDIA/NVFlare.

运动转移旨在将驱动视频的运动转移到源图像。当驾驶视频中的对象与源图像中的对象之间存在很大差异时，传统的单个域运动转移方法通常会产生显着的伪影。例如，合成的图像可能无法保留源图像的人类形状（参见图1（a））。为了解决这个问题，在这项工作中，我们提出了一种运动和外观适应（MAA）进行跨域运动转移的方法，在该方法中，我们将合成图像中的对象正规化，以捕获驾驶框架中对象的运动，而仍保留对象在源图像中的形状和外观。一方面，考虑合成图像和驾驶框架的对象形状可能有所不同，我们设计了一个形状不变的运动适应模块，该模块可以在两个图像中强制对象零件的角度的一致性来捕获运动信息。另一方面，我们引入了一个结构引导的外观一致性模块，旨在使合成图像的相应贴片和源图像之间的相似性正式化，而不会影响合成图像中学习的运动。我们提出的MAA模型可以通过循环重建损失以端到端的方式进行训练，并最终产生令人满意的运动转移结果（参见图1（b））。我们在人类舞蹈数据集Mixamo-Video上进行了广泛的实验，以便于时尚视频和人脸数据集vox-celeb到cufs；在这两个方面，我们的MAA模型在定量和定性上都优于现有方法。

Precision-Recall曲线（AUPRC）下区域的随机优化是机器学习的关键问题。尽管已经对各种算法进行了广泛研究以进行AUPRC优化，但仅在多Query情况下保证了概括。在这项工作中，我们介绍了随机AUPRC优化的一次性概括中的第一个试验。对于更庞大的概括范围，我们专注于算法依赖性概括。我们目的地都有算法和理论障碍。从算法的角度来看，我们注意到，仅当采样策略偏见时，大多数现有随机估计器才会偏向，并且由于不可兼容性而不稳定。为了解决这些问题，我们提出了一个具有卓越稳定性的采样率不变的无偏随机估计器。最重要的是，AUPRC优化是作为组成优化问题配制的，并提出了随机算法来解决此问题。从理论的角度来看，算法依赖性概括分析的标准技术不能直接应用于这种列表的组成优化问题。为了填补这一空白，我们将模型稳定性从实例损失扩展到列表损失，并弥合相应的概括和稳定性。此外，我们构建状态过渡矩阵以描述稳定性的复发，并通过矩阵频谱简化计算。实际上，关于三个图像检索数据集的实验结果谈到了我们框架的有效性和健全性。

最近，基于云的图形卷积网络（GCN）在许多对隐私敏感的应用程序（例如个人医疗保健和金融系统）中表现出了巨大的成功和潜力。尽管在云上具有很高的推理准确性和性能，但在GCN推理中保持数据隐私，这对于这些实际应用至关重要，但仍未得到探索。在本文中，我们对此进行了初步尝试，并开发了$ \ textit {cryptogcn} $ - 基于GCN推理框架的同型加密（HE）。我们方法成功的关键是减少HE操作的巨大计算开销，这可能比明文空间中的同行高的数量级。为此，我们开发了一种方法，可以有效利用GCN推断中基质操作的稀疏性，从而大大减少计算开销。具体而言，我们提出了一种新型的AMA数据格式方法和相关的空间卷积方法，该方法可以利用复杂的图结构并在HE计算中执行有效的矩阵矩阵乘法，从而大大减少HE操作。我们还开发了一个合作式框架，该框架可以通过明智的修剪和GCN中激活模块的多项式近似来探索准确性，安全级别和计算开销之间的交易折扣。基于NTU-Xview骨架关节数据集，即，据我们所知，最大的数据集对同型的评估，我们的实验结果表明，$ \ textit {cryptogcn} $均优胜于最先进的解决方案。同构操作的延迟和数量，即在延迟上达到3.10 $ \ times $加速，并将总代态操作数量减少77.4 \％，而准确度的较小精度损失为1-1.5 $ \％$。

深度学习（DL）的快速增长和部署目睹了新兴的隐私和安全问题。为了减轻这些问题，已经讨论了安全的多方计算（MPC），以实现隐私保护DL计算。在实践中，它们通常是在很高的计算和沟通开销中，并有可能禁止其在大规模系统中的受欢迎程度。两种正交研究趋势吸引了人们对安全深度学习的能源效率的巨大兴趣，即MPC比较方案的高架降低和硬件加速度。但是，他们要么达到较低的减少比率，因此由于计算和通信节省有限而遭受了高潜伏期，或者是渴望的，因为现有的作品主要集中在CPU和GPU等一般计算平台上。在这项工作中，作为第一次尝试，我们通过将加密构件构建块的硬件延迟整合到DNN损耗功能中，以实现高能量效率，开发了一个系统的polympcnet，以减少MPC比较协议和硬件加速的联合额外降低的系统框架Polympcnet。和安全保证。我们的关键设计原理不是在DNN进行良好训练之后（通过删除或删除某些非物质操作员）训练（通过删除或删除某些非物质操作员）之后检查模型敏感性，而是要准确地执行DNN设计中的假设 - 培训DNN既是DNN都硬件有效且安全，同时逃脱了当地的最小值和鞍点并保持高精度。更具体地说，我们提出了通过多项式激活初始化方法直接提出的加密硬件友好的可训练多项式激活功能，以替代昂贵的2P-RELU操作员。我们开发了一个密码硬件调度程序和现场可编程门阵列（FPGA）平台的相应性能模型。

开放世界对象检测是一个更具笼统和挑战性的目标，旨在识别和本地化由任意类别名称描述的对象。最近的工作GLIP通过将检测数据集的所有类别名称连接到句子中，从而将此问题作为接地问题，从而导致类别名称之间的效率低下的相互作用。本文介绍了Distclip，这是一种通过诉诸于设计概念词典的知识富集，是一种平行的视觉概念训练预训练方法，用于开放世界检测。为了提高学习效率，我们提出了一种新型的并行概念公式，该公式分别提取概念，以更好地利用异质数据集（即检测，接地和图像文本对）进行培训。我们进一步设计了来自各种在线资源和检测数据集的概念字典〜（带有描述），以提供每个概念的先验知识。通过用描述丰富这些概念，我们明确地建立了各种概念之间的关系，以促进开放域学习。所提出的概念词典进一步用于提供足够的负面概念，用于构建单词区域对齐损失\，并完成图像对文本对数据标题中缺少描述的对象的标签。所提出的框架显示出强烈的零射击性能性能，例如，在LVIS数据集上，我们的DETCLIP-T优于9.9％的地图GLIPT-T优于GLIP-T，并且与完全避免的型号相比，稀有类别的稀有类别提高了13.5％。作为我们的。