Brain tumor imaging has been part of the clinical routine for many years to perform non-invasive detection and grading of tumors. Tumor segmentation is a crucial step for managing primary brain tumors because it allows a volumetric analysis to have a longitudinal follow-up of tumor growth or shrinkage to monitor disease progression and therapy response. In addition, it facilitates further quantitative analysis such as radiomics. Deep learning models, in particular CNNs, have been a methodology of choice in many applications of medical image analysis including brain tumor segmentation. In this study, we investigated the main design aspects of CNN models for the specific task of MRI-based brain tumor segmentation. Two commonly used CNN architectures (i.e. DeepMedic and U-Net) were used to evaluate the impact of the essential parameters such as learning rate, batch size, loss function, and optimizer. The performance of CNN models using different configurations was assessed with the BraTS 2018 dataset to determine the most performant model. Then, the generalization ability of the model was assessed using our in-house dataset. For all experiments, U-Net achieved a higher DSC compared to the DeepMedic. However, the difference was only statistically significant for whole tumor segmentation using FLAIR sequence data and tumor core segmentation using T1w sequence data. Adam and SGD both with the initial learning rate set to 0.001 provided the highest segmentation DSC when training the CNN model using U-Net and DeepMedic architectures, respectively. No significant difference was observed when using different normalization approaches. In terms of loss functions, a weighted combination of soft Dice and cross-entropy loss with the weighting term set to 0.5 resulted in an improved segmentation performance and training stability for both DeepMedic and U-Net models.

Analogical proportions compare pairs of items (a, b) and (c, d) in terms of their differences and similarities. They play a key role in the formalization of analogical inference. The paper first discusses how to improve analogical inference in terms of accuracy and in terms of computational cost. Then it indicates the potential of analogical proportions for explanation. Finally, it highlights the close relationship between analogical proportions and multi-valued dependencies, which reveals an unsuspected aspect of the former.

A wide variety of model explanation approaches have been proposed in recent years, all guided by very different rationales and heuristics. In this paper, we take a new route and cast interpretability as a statistical inference problem. We propose a general deep probabilistic model designed to produce interpretable predictions. The model parameters can be learned via maximum likelihood, and the method can be adapted to any predictor network architecture and any type of prediction problem. Our method is a case of amortized interpretability models, where a neural network is used as a selector to allow for fast interpretation at inference time. Several popular interpretability methods are shown to be particular cases of regularised maximum likelihood for our general model. We propose new datasets with ground truth selection which allow for the evaluation of the features importance map. Using these datasets, we show experimentally that using multiple imputation provides more reasonable interpretations.

This article focuses on the control center of each human body: the brain. We will point out the pivotal role of the cerebral vasculature and how its complex mechanisms may vary between subjects. We then emphasize a specific acute pathological state, i.e., acute ischemic stroke, and show how medical imaging and its analysis can be used to define the treatment. We show how the core-penumbra concept is used in practice using mismatch criteria and how machine learning can be used to make predictions of the final infarct, either via deconvolution or convolutional neural networks.

The clinical interest is often to measure the volume of a structure, which is typically derived from a segmentation. In order to evaluate and compare segmentation methods, the similarity between a segmentation and a predefined ground truth is measured using popular discrete metrics, such as the Dice score. Recent segmentation methods use a differentiable surrogate metric, such as soft Dice, as part of the loss function during the learning phase. In this work, we first briefly describe how to derive volume estimates from a segmentation that is, potentially, inherently uncertain or ambiguous. This is followed by a theoretical analysis and an experimental validation linking the inherent uncertainty to common loss functions for training CNNs, namely cross-entropy and soft Dice. We find that, even though soft Dice optimization leads to an improved performance with respect to the Dice score and other measures, it may introduce a volume bias for tasks with high inherent uncertainty. These findings indicate some of the method's clinical limitations and suggest doing a closer ad-hoc volume analysis with an optional re-calibration step.

通常通过后处理，涉及降低和后续可视化来解释高维数据的聚类结果。这破坏了数据的含义并混淆了解释。我们提出了算法 - 敏捷的解释方法，以在缩小尺寸中解释聚类结果，同时保留数据的完整性。集群的置换特征重要性代表基于改组特征值并通过自定义分数功能衡量群集分配的变化的一般框架。集群的个体条件期望表明由于数据的变化而导致群集分配的观察变化。聚类的部分依赖性评估整个特征空间的群集分配的平均变化。所有方法都可以与能够通过软标签重新分配实例的任何聚类算法一起使用。与常见的后处理方法（例如主组件分析）相反，引入的方法保持了特征的原始结构。

破产预测的模型在几种现实世界情景中很有用，并且基于结构化（数值）以及非结构化（文本）数据，已经为任务提供了多个研究贡献。但是，缺乏常见的基准数据集和评估策略阻碍了模型之间的客观比较。本文基于新颖和已建立的数据集为非结构化数据方案介绍了这样的基准，以刺激对任务的进一步研究。我们描述和评估几种经典和神经基线模型，并讨论不同策略的好处和缺陷。特别是，我们发现基于静态内域字表示的轻巧的单词袋模型可获得令人惊讶的良好结果，尤其是在考虑几年中的文本数据时。这些结果进行了严格的评估，并根据数据的特定方面和任务进行了讨论。复制数据的所有代码，将发布实验结果。

进化算法通常通过交叉和突变探索解决方案的搜索空间。虽然突变由溶液的局部局部修饰组成，但跨界将两种溶液的遗传信息混合在一起，以计算新的溶液。对于模型驱动的优化（MDO），模型直接提供了可能的解决方案（而不是首先将它们转换为另一种表示），仅开发了一个通用的跨界操作员。我们将图形作为模型的正式基础，我们进一步完善了该操作员的方式，以至于保留了其他良好形式的约束：我们证明，给定两个模型满足给定的一组多重性约束作为输入，我们的精制交叉运算符计算两个新模型作为输出，也满足一组约束。

最近的工作表明，具有$ M $组件的有限混合模型是可识别的，而在混合组件上没有假设，只要一个人可以访问$ 2M-1 $的样本，这些样本来自同一$混合物组件。在这项工作中，我们概括了结果，并表明，如果混合模型的$ K $混合组件的每个子集都是线性独立的，则该混合模型仅可识别，只有$（2M-1）/（k-1）$样品每组。我们进一步表明该值无法改善。我们证明了一种类似的结果，即一种更强的可识别性形式，即“确定性”以及相应的下限。如果混合组件是从$ k $维空间随机选择的，那么这种独立性的假设几乎可以肯定地。我们描述了我们对多项式混合模型和主题建模的结果的一些含义。

尽管骰子损失是医学图像分割中的主要损失函数之一，但大多数研究都忽略了其导数，即使用梯度下降时优化的真实电动机。在本文中，我们强调了在缺少或空的标签存在下骰子丢失的特殊作用。首先，我们制定一个理论基础，对骰子丢失及其导数进行了一般描述。事实证明，减少尺寸$ \ phi $和平滑项$ \ epsilon $的选择是无处不在的，并且极大地影响了其行为。我们找到并提出了$ \ phi $和$ \ epsilon $的启发式组合，它们在细分设置中使用，带有缺失或空标签。其次，我们使用两个公开可用的数据集在二进制和多类分段设置中验证这些发现。我们确认，$ \ phi $和$ \ epsilon $的选择确实是关键的。选择了$ \ phi $，因此减少的单个元素（和类）元素以及可忽略不计的$ \ epsilon $进行，骰子损失与缺失的标签自然交易，并且与最近缺少标签的最新适应性相似。选择$ \ phi $，以使减少量发生在多个批处理元素上，或以$ \ epsilon $的启发式值进行，骰子损失正确处理空标签。我们认为，这项工作强调了一些基本观点，并希望它鼓励研究人员更好地描述他们对未来工作中骰子损失的确切实施。