在线学习算法已成为机器学习工具箱中的无处不在的工具，并且经常用于小资源约束环境。在最成功的在线学习方法中，是决策树（DT）合奏。 DT集合提供出色的性能，同时适应数据的变化，但它们不是资源高效。增量树学习者将新节点添加到树中，但从不删除旧的节点随着时间的推移增加内存消耗。另一方面，基于梯度的树学习需要计算整个树上的渐变，这对于甚至是适度尺寸的树木而成本。在本文中，我们提出了一种新的记忆有效的在线分类集合，称为资源约束系统。我们的算法在小窗户上培训到中型决策树，并使用随机近端梯度下降来学习这些`灌木的合奏重量。我们对我们的算法提供了一个理论分析，并包括对在线环境中的方法的行为进行了广泛的讨论。在12个不同的数据集中的一系列2〜959实验中，我们将我们的方法与8种最先进的方法进行比较。我们的灌木合奏即使只有很少的内存都有良好的性能也可以保留出色的性能。我们展示SE在12例中提供了更好的准确性记忆权衡，同时具有比大多数其他方法的统计学显着更好的性能。我们的实现是在https://github.com/sbuschjaeger/se-online下获得的。

随机森林（RFS）是机器学习中最先进的，并且具有近零参数调整的优异性能。值得注意的是，即使他们的基本构建块被众所周知，RFS似乎不受限制地过度装修。最近，广泛接受的研究认为，RF呈现所谓的双下降曲线：首先，模型将数据过于U形曲线中的数据，然后，一旦达到某种模型复杂性，它就突然改善了其性能。在本文中，我们挑战模型能力是解释RF成功的正确工具的概念，并争辩说该模型的算法比以前认为更重要的作用。我们表明RF没有表现出双重曲线，而是单个下降。因此，在经典意义上没有过度装备。我们进一步提出了RF变化，尽管其决策边界近似于过度啮合的DT。类似，我们表明，近似于RF的决策边界的DT仍将过度装备。最后，我们研究了整体的多样性作为估计其性能的工具。为此，我们引入负相关森林（NClest），允许精确控制集合中的多样性。我们表明，多样性和偏差确实对RF的性能产生了至关重要的影响。具有太小的多样性将RF的性能坍塌到一棵树中，而具有太多的多样性意味着大多数树木不会再产生正确的输出。然而，在这两个极端之间，我们发现了大量不同的权衡，具有大致相等的性能。因此，只要算法达到这种良好的权衡制度，偏差和多样性之间的特定权衡并不重要。

Machine learning applications have become ubiquitous. Their applications range from embedded control in production machines over process optimization in diverse areas (e.g., traffic, finance, sciences) to direct user interactions like advertising and recommendations. This has led to an increased effort of making machine learning trustworthy. Explainable and fair AI have already matured. They address the knowledgeable user and the application engineer. However, there are users that want to deploy a learned model in a similar way as their washing machine. These stakeholders do not want to spend time in understanding the model, but want to rely on guaranteed properties. What are the relevant properties? How can they be expressed to the stakeholder without presupposing machine learning knowledge? How can they be guaranteed for a certain implementation of a machine learning model? These questions move far beyond the current state of the art and we want to address them here. We propose a unified framework that certifies learning methods via care labels. They are easy to understand and draw inspiration from well-known certificates like textile labels or property cards of electronic devices. Our framework considers both, the machine learning theory and a given implementation. We test the implementation's compliance with theoretical properties and bounds.

The release of ChatGPT, a language model capable of generating text that appears human-like and authentic, has gained significant attention beyond the research community. We expect that the convincing performance of ChatGPT incentivizes users to apply it to a variety of downstream tasks, including prompting the model to simplify their own medical reports. To investigate this phenomenon, we conducted an exploratory case study. In a questionnaire, we asked 15 radiologists to assess the quality of radiology reports simplified by ChatGPT. Most radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed key medical findings, and potentially harmful passages were reported. While further studies are needed, the initial insights of this study indicate a great potential in using large language models like ChatGPT to improve patient-centered care in radiology and other medical domains.

Local patterns play an important role in statistical physics as well as in image processing. Two-dimensional ordinal patterns were studied by Ribeiro et al. who determined permutation entropy and complexity in order to classify paintings and images of liquid crystals. Here we find that the 2 by 2 patterns of neighboring pixels come in three types. The statistics of these types, expressed by two parameters, contains the relevant information to describe and distinguish textures. The parameters are most stable and informative for isotropic structures.

Many dialogue systems (DSs) lack characteristics humans have, such as emotion perception, factuality, and informativeness. Enhancing DSs with knowledge alleviates this problem, but, as many ways of doing so exist, keeping track of all proposed methods is difficult. Here, we present the first survey of knowledge-enhanced DSs. We define three categories of systems - internal, external, and hybrid - based on the knowledge they use. We survey the motivation for enhancing DSs with knowledge, used datasets, and methods for knowledge search, knowledge encoding, and knowledge incorporation. Finally, we propose how to improve existing systems based on theories from linguistics and cognitive science.

Attention-based multiple instance learning (AMIL) algorithms have proven to be successful in utilizing gigapixel whole-slide images (WSIs) for a variety of different computational pathology tasks such as outcome prediction and cancer subtyping problems. We extended an AMIL approach to the task of survival prediction by utilizing the classical Cox partial likelihood as a loss function, converting the AMIL model into a nonlinear proportional hazards model. We applied the model to tissue microarray (TMA) slides of 330 lung cancer patients. The results show that AMIL approaches can handle very small amounts of tissue from a TMA and reach similar C-index performance compared to established survival prediction methods trained with highly discriminative clinical factors such as age, cancer grade, and cancer stage

Nucleolar organizer regions (NORs) are parts of the DNA that are involved in RNA transcription. Due to the silver affinity of associated proteins, argyrophilic NORs (AgNORs) can be visualized using silver-based staining. The average number of AgNORs per nucleus has been shown to be a prognostic factor for predicting the outcome of many tumors. Since manual detection of AgNORs is laborious, automation is of high interest. We present a deep learning-based pipeline for automatically determining the AgNOR-score from histopathological sections. An additional annotation experiment was conducted with six pathologists to provide an independent performance evaluation of our approach. Across all raters and images, we found a mean squared error of 0.054 between the AgNOR- scores of the experts and those of the model, indicating that our approach offers performance comparable to humans.

Mitotic activity is key for the assessment of malignancy in many tumors. Moreover, it has been demonstrated that the proportion of abnormal mitosis to normal mitosis is of prognostic significance. Atypical mitotic figures (MF) can be identified morphologically as having segregation abnormalities of the chromatids. In this work, we perform, for the first time, automatic subtyping of mitotic figures into normal and atypical categories according to characteristic morphological appearances of the different phases of mitosis. Using the publicly available MIDOG21 and TUPAC16 breast cancer mitosis datasets, two experts blindly subtyped mitotic figures into five morphological categories. Further, we set up a state-of-the-art object detection pipeline extending the anchor-free FCOS approach with a gated hierarchical subclassification branch. Our labeling experiment indicated that subtyping of mitotic figures is a challenging task and prone to inter-rater disagreement, which we found in 24.89% of MF. Using the more diverse MIDOG21 dataset for training and TUPAC16 for testing, we reached a mean overall average precision score of 0.552, a ROC AUC score of 0.833 for atypical/normal MF and a mean class-averaged ROC-AUC score of 0.977 for discriminating the different phases of cells undergoing mitosis.

Modern machine learning models are often constructed taking into account multiple objectives, e.g., to minimize inference time while also maximizing accuracy. Multi-objective hyperparameter optimization (MHPO) algorithms return such candidate models and the approximation of the Pareto front is used to assess their performance. However, when estimating generalization performance of an approximation of a Pareto front found on a validation set by computing the performance of the individual models on the test set, models might no longer be Pareto-optimal. This makes it unclear how to measure performance. To resolve this, we provide a novel evaluation protocol that allows measuring the generalization performance of MHPO methods and to study its capabilities for comparing two optimization experiments.