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.

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。

We present a Machine Learning (ML) study case to illustrate the challenges of clinical translation for a real-time AI-empowered echocardiography system with data of ICU patients in LMICs. Such ML case study includes data preparation, curation and labelling from 2D Ultrasound videos of 31 ICU patients in LMICs and model selection, validation and deployment of three thinner neural networks to classify apical four-chamber view. Results of the ML heuristics showed the promising implementation, validation and application of thinner networks to classify 4CV with limited datasets. We conclude this work mentioning the need for (a) datasets to improve diversity of demographics, diseases, and (b) the need of further investigations of thinner models to be run and implemented in low-cost hardware to be clinically translated in the ICU in LMICs. The code and other resources to reproduce this work are available at https://github.com/vital-ultrasound/ai-assisted-echocardiography-for-low-resource-countries.

Like fingerprints, cortical folding patterns are unique to each brain even though they follow a general species-specific organization. Some folding patterns have been linked with neurodevelopmental disorders. However, due to the high inter-individual variability, the identification of rare folding patterns that could become biomarkers remains a very complex task. This paper proposes a novel unsupervised deep learning approach to identify rare folding patterns and assess the degree of deviations that can be detected. To this end, we preprocess the brain MR images to focus the learning on the folding morphology and train a beta-VAE to model the inter-individual variability of the folding. We compare the detection power of the latent space and of the reconstruction errors, using synthetic benchmarks and one actual rare configuration related to the central sulcus. Finally, we assess the generalization of our method on a developmental anomaly located in another region. Our results suggest that this method enables encoding relevant folding characteristics that can be enlightened and better interpreted based on the generative power of the beta-VAE. The latent space and the reconstruction errors bring complementary information and enable the identification of rare patterns of different nature. This method generalizes well to a different region on another dataset. Code is available at https://github.com/neurospin-projects/2022_lguillon_rare_folding_detection.

本文描述了（r）ules（o）f（t）he（r）oad（a）dvisor，该代理提供了推荐的和可能从一组人级规则生成的动作。我们以形式和示例描述了Rotra的架构和设计。具体来说，我们使用Rotra正式化和实施英国“道路规则”，并描述如何将其纳入自动驾驶汽车中，从而可以内部推荐遵守道路规则。此外，根据《英国公路法典》（《道路规则》），规定规则是否必须采取行动，或者仅建议采取行动，以指示生成的可能的措施。利用该系统的好处包括能够适应不同司法管辖区的不同法规；允许从规则到行为的清晰可追溯性，并提供外部自动责任机制，可以检查在某些给定情况下是否遵守规则。通过具体的示例，对自动驾驶汽车的模拟显示如何通过将自动驾驶汽车放置在许多情况下，这些场景测试了汽车遵守道路规则的能力。合并该系统的自动驾驶汽车能够确保他们遵守道路和外部（法律或监管机构的规则透明工作，从而使汽车公司，司法管辖区和公众之间的信任更大。

本文比较了软件定义网络中的网络安全性的两种深入强化学习方法。对深Q网络的神经情节控制已实施，并将其与双重深Q网络进行了比较。这两种算法以类似于零和游戏的格式实现。对两个游戏结果进行了两尾t检验分析，其中包含为防守者赢得的冠军的数量。另一个比较是在各自游戏中代理商的游戏得分上进行的。进行分析是为了确定哪种算法是游戏表演者最好的算法，以及它们之间是否存在显着差异，证明一个算法是否会更偏爱另一个。发现两种方法之间没有显着统计差异。

从机器学习的角度来看，当前的语音识别体系结构的表现非常出色，因此用户互动。这表明他们很好地模拟了人类生物系统。我们调查是否可以颠倒推论以提供对该生物系统的见解。特别是听力机制。使用SINCNET，我们确认端到端系统确实学习了众所周知的滤纸结构。但是，我们还表明，在学习结构中，更宽的带宽过滤器很重要。虽然可以通过初始化狭窄和宽带过滤器来获得一些好处，但生理上的限制表明，这种过滤器是在中脑而不是耳蜗中出现的。我们表明，必须修改标准的机器学习体系结构，以允许神经模拟此过程。

当前的对比学习方法使用从大量转换列表（固定的超参数）中采样的随机转换来从未经注释的数据库中学习不变性。遵循以前引入少量监督的作品，我们提出了一个框架，以找到使用可区分转换网络的对比度学习的最佳转换。我们的方法在监督准确性和收敛速度方面都在低注释的数据制度下提高了性能。与以前的工作相反，转换优化不需要生成模型。转换的图像保留相关信息以解决监督任务，此处分类。在34000 2D切片的大脑磁共振图像和11200胸X射线图像上进行实验。在两个数据集（具有标记数据的10％）上，我们的模型比具有100％标签的完全监督模型获得了更好的性能。

多模式学习通过在预测过程中同样组合多个输入数据模式来重点关注培训模型。但是，这种相等的组合可能不利于预测准确性，因为不同的方式通常伴随着不同水平的不确定性。通过几种方法研究了使用这种不确定性来组合模式，但是成功有限，因为这些方法旨在处理特定的分类或细分问题，并且不能轻易地转化为其他任务，或者遭受数值的不稳定性。在本文中，我们提出了一种新的不确定性多模式学习者，该学习者通过通过跨模式随机网络预测（CRNP）测量特征密度来估计不确定性。 CRNP旨在几乎不需要适应来在不同的预测任务之间转换，同时进行稳定的培训过程。从技术角度来看，CRNP是探索随机网络预测以估算不确定性并结合多模式数据的第一种方法。对两个3D多模式医学图像分割任务和三个2D多模式计算机视觉分类任务的实验显示了CRNP的有效性，适应性和鲁棒性。此外，我们提供了有关不同融合功能和可视化的广泛讨论，以验证提出的模型。