深度学习的概括分析通常假定训练会收敛到固定点。但是，最近的结果表明，实际上，用随机梯度下降优化的深神经网络的权重通常无限期振荡。为了减少理论和实践之间的这种差异，本文着重于神经网络的概括，其训练动力不一定会融合到固定点。我们的主要贡献是提出一个统计算法稳定性（SAS）的概念，该算法将经典算法稳定性扩展到非convergergent算法并研究其与泛化的联系。与传统的优化和学习理论观点相比，这种崇高的理论方法可导致新的见解。我们证明，学习算法的时间复杂行为的稳定性与其泛化有关，并在经验上证明了损失动力学如何为概括性能提供线索。我们的发现提供了证据表明，即使训练无限期继续并且权重也不会融合，即使训练持续进行训练，训练更好地概括”的网络也是如此。

将离散域上的功能集成到神经网络中是开发其推理离散对象的能力的关键。但是，离散域是（1）自然不适合基于梯度的优化，并且（2）与依赖于高维矢量空间中表示形式的深度学习体系结构不相容。在这项工作中，我们解决了设置功能的两个困难，这些功能捕获了许多重要的离散问题。首先，我们开发了将设置功能扩展到低维连续域的框架，在该域中，许多扩展是自然定义的。我们的框架包含许多众所周知的扩展，作为特殊情况。其次，为避免不良的低维神经网络瓶颈，我们将低维扩展转换为高维空间中的表示形式，从半际计划进行组合优化的成功中获得了灵感。从经验上讲，我们观察到扩展对无监督的神经组合优化的好处，特别是具有高维其表示。

我们从光谱的角度解决图形生成问题，首先生成图形laplacian光谱的主要部分，然后构建与这些特征值和特征向量相匹配的图。光谱调节允许直接建模全局和局部图结构，并有助于克服单发图生成器的表达性和模式崩溃问题。我们的新颖的甘（Spectre）称为Spectre，可以使用一声模型来产生比以前可能更大的图。Spectre的表现优于最先进的深度自动回归发电机在建模忠诚方面，同时还避免了昂贵的顺序产生和对节点排序的依赖。一个很好的例子，在相当大的合成和现实图形中，Specter的幽灵比最佳竞争对手的最佳竞争对手的改进是4到170倍，该竞争对手不合适，比自回旋发电机快23至30倍。

我们可以使用机器学习来压缩图形数据吗？在图中没有排序对传统压缩算法构成了重大挑战，限制了其可达到的收益以及他们发现相关模式的能力。另一方面，大多数图表压缩方法依赖于域依赖的手工制作表示，并且无法适应不同的底层图分布。这项工作旨在建立必要的原则，无损图形压缩方法应遵循以接近熵储存下限。我们不是对图形分布进行僵化的假设，我们将压缩机作为概率模型制定，可以从数据学习并概括到看不见的实例。我们的“分区和代码”框架需要三个步骤：首先，分区算法将图形分解为子图，然后映射到我们学习概率分布的小词典的元素，最后，熵编码器转换了表示进入比特。所有组件（分区，字典和分发）都是参数化的，可以用梯度下降训练。理论上，从温和条件下理论上比较了几个图形编码的压缩质量，并证明了PNC实现了线性或二次以顶点的数量而产生的压缩增益。经验上，PNC对不同的现实网络产生了显着的压缩改进。

Recent trends of incorporating attention mechanisms in vision have led researchers to reconsider the supremacy of convolutional layers as a primary building block. Beyond helping CNNs to handle long-range dependencies, Ramachandran et al. (2019) showed that attention can completely replace convolution and achieve state-of-the-art performance on vision tasks. This raises the question: do learned attention layers operate similarly to convolutional layers? This work provides evidence that attention layers can perform convolution and, indeed, they often learn to do so in practice. Specifically, we prove that a multi-head self-attention layer with sufficient number of heads is at least as expressive as any convolutional layer. Our numerical experiments then show that self-attention layers attend to pixel-grid patterns similarly to CNN layers, corroborating our analysis. Our code is publicly available 1 .

Coronary Computed Tomography Angiography (CCTA) provides information on the presence, extent, and severity of obstructive coronary artery disease. Large-scale clinical studies analyzing CCTA-derived metrics typically require ground-truth validation in the form of high-fidelity 3D intravascular imaging. However, manual rigid alignment of intravascular images to corresponding CCTA images is both time consuming and user-dependent. Moreover, intravascular modalities suffer from several non-rigid motion-induced distortions arising from distortions in the imaging catheter path. To address these issues, we here present a semi-automatic segmentation-based framework for both rigid and non-rigid matching of intravascular images to CCTA images. We formulate the problem in terms of finding the optimal \emph{virtual catheter path} that samples the CCTA data to recapitulate the coronary artery morphology found in the intravascular image. We validate our co-registration framework on a cohort of $n=40$ patients using bifurcation landmarks as ground truth for longitudinal and rotational registration. Our results indicate that our non-rigid registration significantly outperforms other co-registration approaches for luminal bifurcation alignment in both longitudinal (mean mismatch: 3.3 frames) and rotational directions (mean mismatch: 28.6 degrees). By providing a differentiable framework for automatic multi-modal intravascular data fusion, our developed co-registration modules significantly reduces the manual effort required to conduct large-scale multi-modal clinical studies while also providing a solid foundation for the development of machine learning-based co-registration approaches.

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.

Artificial Intelligence (AI) has become commonplace to solve routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload on radiologists is steadily increasing. We project that the gap between the number of imaging exams and the number of expert radiologist readers required to cover this increase will continue to expand, consequently introducing a demand for AI-based tools that improve the efficiency with which radiologists can comfortably interpret these exams. AI has been shown to improve efficiency in medical-image generation, processing, and interpretation, and a variety of such AI models have been developed across research labs worldwide. However, very few of these, if any, find their way into routine clinical use, a discrepancy that reflects the divide between AI research and successful AI translation. To address the barrier to clinical deployment, we have formed MONAI Consortium, an open-source community which is building standards for AI deployment in healthcare institutions, and developing tools and infrastructure to facilitate their implementation. This report represents several years of weekly discussions and hands-on problem solving experience by groups of industry experts and clinicians in the MONAI Consortium. We identify barriers between AI-model development in research labs and subsequent clinical deployment and propose solutions. Our report provides guidance on processes which take an imaging AI model from development to clinical implementation in a healthcare institution. We discuss various AI integration points in a clinical Radiology workflow. We also present a taxonomy of Radiology AI use-cases. Through this report, we intend to educate the stakeholders in healthcare and AI (AI researchers, radiologists, imaging informaticists, and regulators) about cross-disciplinary challenges and possible solutions.

The future of population-based breast cancer screening is likely personalized strategies based on clinically relevant risk models. Mammography-based risk models should remain robust to domain shifts caused by different populations and mammographic devices. Modern risk models do not ensure adaptation across vendor-domains and are often conflated to unintentionally rely on both precursors of cancer and systemic/global mammographic information associated with short- and long-term risk, respectively, which might limit performance. We developed a robust, cross-vendor model for long-term risk assessment. An augmentation-based domain adaption technique, based on flavorization of mammographic views, ensured generalization to an unseen vendor-domain. We trained on samples without diagnosed/potential malignant findings to learn systemic/global breast tissue features, called mammographic texture, indicative of future breast cancer. However, training so may cause erratic convergence. By excluding noise-inducing samples and designing a case-control dataset, a robust ensemble texture model was trained. This model was validated in two independent datasets. In 66,607 Danish women with flavorized Siemens views, the AUC was 0.71 and 0.65 for prediction of interval cancers within two years (ICs) and from two years after screening (LTCs), respectively. In a combination with established risk factors, the model's AUC increased to 0.68 for LTCs. In 25,706 Dutch women with Hologic-processed views, the AUCs were not different from the AUCs in Danish women with flavorized views. The results suggested that the model robustly estimated long-term risk while adapting to an unseen processed vendor-domain. The model identified 8.1% of Danish women accounting for 20.9% of ICs and 14.2% of LTCs.

Quaternion valued neural networks experienced rising popularity and interest from researchers in the last years, whereby the derivatives with respect to quaternions needed for optimization are calculated as the sum of the partial derivatives with respect to the real and imaginary parts. However, we can show that product- and chain-rule does not hold with this approach. We solve this by employing the GHRCalculus and derive quaternion backpropagation based on this. Furthermore, we experimentally prove the functionality of the derived quaternion backpropagation.