机器学习（ML）涵盖的实验必须考虑评估模型性能的两个重要方面：数据集和算法。需要强大的基准来评估最佳分类器。为此，可以采用公共存储库中提供的金标准基准。但是，常常不考虑在评估时考虑数据集的复杂性。这项工作提出了一种基于物品响应理论（IRT）和GLICKO-2的组合的新评估方法，该方法通常采用了评估参与者的强度（例如，国际象棋）。对于基准测试中的每个数据集，IRT用于估计分类器的能力，良好的分类器对最困难的测试实例具有良好的预测。然后为每对分类器运行锦标赛，以便GLICKO-2更新每个分类器等额定值，评级偏差和波动等性能信息。在此进行了一个案例研究，该研究通过了OpenML-CC18基准作为数据集的集合和各种分类算法的池进行评估。并非所有数据集都被观察到对评估算法非常有用，其中只有10％被认为是非常困难的。此外，验证了仅包含50％的OpenML-CC18的50％的子集的存在，其同样有用于算法评估。关于算法，本文提出的方法将随机林识别为具有最佳天生能力的算法。

We describe a Physics-Informed Neural Network (PINN) that simulates the flow induced by the astronomical tide in a synthetic port channel, with dimensions based on the Santos - S\~ao Vicente - Bertioga Estuarine System. PINN models aim to combine the knowledge of physical systems and data-driven machine learning models. This is done by training a neural network to minimize the residuals of the governing equations in sample points. In this work, our flow is governed by the Navier-Stokes equations with some approximations. There are two main novelties in this paper. First, we design our model to assume that the flow is periodic in time, which is not feasible in conventional simulation methods. Second, we evaluate the benefit of resampling the function evaluation points during training, which has a near zero computational cost and has been verified to improve the final model, especially for small batch sizes. Finally, we discuss some limitations of the approximations used in the Navier-Stokes equations regarding the modeling of turbulence and how it interacts with PINNs.

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 the impact of psychiatric disorders on clinical health, early-stage diagnosis remains a challenge. Machine learning studies have shown that classifiers tend to be overly narrow in the diagnosis prediction task. The overlap between conditions leads to high heterogeneity among participants that is not adequately captured by classification models. To address this issue, normative approaches have surged as an alternative method. By using a generative model to learn the distribution of healthy brain data patterns, we can identify the presence of pathologies as deviations or outliers from the distribution learned by the model. In particular, deep generative models showed great results as normative models to identify neurological lesions in the brain. However, unlike most neurological lesions, psychiatric disorders present subtle changes widespread in several brain regions, making these alterations challenging to identify. In this work, we evaluate the performance of transformer-based normative models to detect subtle brain changes expressed in adolescents and young adults. We trained our model on 3D MRI scans of neurotypical individuals (N=1,765). Then, we obtained the likelihood of neurotypical controls and psychiatric patients with early-stage schizophrenia from an independent dataset (N=93) from the Human Connectome Project. Using the predicted likelihood of the scans as a proxy for a normative score, we obtained an AUROC of 0.82 when assessing the difference between controls and individuals with early-stage schizophrenia. Our approach surpassed recent normative methods based on brain age and Gaussian Process, showing the promising use of deep generative models to help in individualised analyses.

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

深度神经网络在医学图像分析中带来了显着突破。但是，由于其渴望数据的性质，医学成像项目中适度的数据集大小可能会阻碍其全部潜力。生成合成数据提供了一种有希望的替代方案，可以补充培训数据集并进行更大范围的医学图像研究。最近，扩散模型通过产生逼真的合成图像引起了计算机视觉社区的注意。在这项研究中，我们使用潜在扩散模型探索从高分辨率3D脑图像中生成合成图像。我们使用来自英国生物银行数据集的T1W MRI图像（n = 31,740）来训练我们的模型，以了解脑图像的概率分布，该脑图像以协变量为基础，例如年龄，性别和大脑结构量。我们发现我们的模型创建了现实的数据，并且可以使用条件变量有效地控制数据生成。除此之外，我们创建了一个带有100,000次脑图像的合成数据集，并使科学界公开使用。

能够分析和量化人体或行为特征的系统（称为生物识别系统）正在使用和应用变异性增长。由于其从手工制作的功能和传统的机器学习转变为深度学习和自动特征提取，因此生物识别系统的性能增加到了出色的价值。尽管如此，这种快速进步的成本仍然尚不清楚。由于其不透明度，深层神经网络很难理解和分析，因此，由错误动机动机动机的隐藏能力或决定是潜在的风险。研究人员已经开始将注意力集中在理解深度神经网络及其预测的解释上。在本文中，我们根据47篇论文的研究提供了可解释生物识别技术的当前状态，并全面讨论了该领域的发展方向。

变形攻击是不断影响深度识别系统的众多威胁之一。它包括从不同个体中选择两张面，并将它们融合到包含两者的身份信息的最终图像中。在这项工作中，我们提出了一个新颖的正规化术语，该术语考虑了两者中存在的身份信息，并促进了两个正交潜在媒介的创建。我们在FRLL数据集中评估了我们提出的方法（Orthomad），并在五个不同的数据集中培训时评估了模型的性能。我们以小的RESNET-18为骨干，我们实现了大多数实验的最新结果，而竞争性则在其他实验中结果。本文的代码将公开可用。

本文介绍了基于2022年国际生物识别技术联合会议（IJCB 2022）举行的基于隐私感知合成训练数据（SYN-MAD）的面部变形攻击检测的摘要。该竞赛吸引了来自学术界和行业的12个参与团队，并在11个不同的国家 /地区举行。最后，参与团队提交了七个有效的意见书，并由组织者进行评估。竞争是为了介绍和吸引解决方案的解决方案，这些解决方案涉及检测面部变形攻击的同时，同时出于道德和法律原因保护人们的隐私。为了确保这一点，培训数据仅限于组织者提供的合成数据。提交的解决方案提出了创新，导致在许多实验环境中表现优于所考虑的基线。评估基准现在可在以下网址获得：https：//github.com/marcohuber/syn-mad-2022。

这项工作总结了2022年2022年国际生物识别联合会议（IJCB 2022）的IJCB被遮挡的面部识别竞赛（IJCB-OCFR-2022）。OCFR-2022从学术界吸引了总共3支参与的团队。最终，提交了六个有效的意见书，然后由组织者评估。在严重的面部阻塞面前，举行了竞争是为了应对面部识别的挑战。参与者可以自由使用任何培训数据，并且通过使用众所周知的数据集构成面部图像的部分来构建测试数据。提交的解决方案提出了创新，并以所考虑的基线表现出色。这项竞争的主要输出是具有挑战性，现实，多样化且公开可用的遮挡面部识别基准，并具有明确的评估协议。