In this study the authors will look at the detection and segmentation of the iris and its influence on the overall performance of the iris-biometric tool chain. The authors will examine whether the segmentation accuracy, based on conformance with a ground truth, can serve as a predictor for the overall performance of the iris-biometric tool chain. That is: If the segmentation accuracy is improved will this always improve the overall performance? Furthermore, the authors will systematically evaluate the influence of segmentation parameters, pupillary and limbic boundary and normalisation centre (based on Daugman's rubbersheet model), on the rest of the iris-biometric tool chain. The authors will investigate if accurately finding these parameters is important and how consistency, that is, extracting the same exact region of the iris during segmenting, influences the overall performance.

尽管机器学习取得了巨大进步（ML），但数据不平衡的培训仍然在许多现实世界中构成挑战。在解决此问题的一系列不同技术中，采样算法被视为有效的解决方案。但是，问题更为根本，许多作品强调了实例硬度的重要性。这个问题是指管理不安全或可能嘈杂的实例的重要性，这些实例更可能被错误分类并作为分类绩效不佳的根本原因。本文介绍了Hardvis，这是一种视觉分析系统，旨在处理实例硬度，主要在分类场景中。我们提出的系统协助用户在视觉上比较数据类型的不同分布，根据局部特征选择实例类型，这些实例后来将受主动采样方法的影响，并验证来自底漆或过采样技术的建议对ML模型有益。此外，我们允许用户找到和采样轻松且难以对所有课程的培训实例进行分类，而不是统一地采样/过采样。用户可以从不同角度探索数据子集以决定所有这些参数，而HardVis则跟踪其步骤并评估模型在测试集中分别评估模型的预测性能。最终结果是一个均衡的数据集，可增强ML模型的预测能力。通过假设使用情况和用例证明了Hardvis的功效和有效性。最后，我们还研究了系统的有用，基于我们从ML专家那里收到的反馈。

创成对抗性网络（甘斯）的主要目标是产生相同的统计数据所提供的培训数据的新数据。然而，最近的多部作品表明，国家的最先进的架构又斗争，以实现这一目标。特别地，他们报告的升高量在光谱统计这使得它可以直接区分真实和生成的图像的高频率。对于这种现象的解释是有争议的：虽然大多数的作品属性文物发电机，其他作品指向鉴别。我们需要在这些解释清醒的审视，并提供有关什么使有效的打击高频文物提出的措施的见解。要做到这一点，我们首先独立评估发电机和鉴别两者的架构，如果他们表现出的频率偏差，使学习的高频含量尤其成问题的分布调查。基于这些实验中，我们提出以下四点看法：1）不同的采样操作偏向不同光谱特性的发电机。 2）由上采样引入的伪像棋盘不能单独解释的光谱差异作为发电机能够补偿这些伪影。 3）鉴别器不与检测本身高频纠缠，但具有低幅度的频率上而奋斗。 4）在鉴别器的下采样操作可以削弱它提供的训练信号的质量。在这些研究结果，我们分析提出了在国家的最先进的甘训练对高频文物的措施，但发现没有现有的方法可以彻底解决谱伪呢。我们的研究结果表明，有很大的潜力，在提高鉴别和，这可能是关键的训练数据的分布更紧密地匹配。

神经随机微分方程（NSDES）模拟随机过程作为神经网络的漂移和扩散函数。尽管已知NSDE可以进行准确的预测，但到目前为止，其不确定性定量属性仍未探索。我们报告了经验发现，即从NSDE获得良好的不确定性估计是计算上的过度估计。作为一种补救措施，我们开发了一种计算负担得起的确定性方案，该方案在动力学受NSD管辖时准确地近似过渡内核。我们的方法引入了匹配算法的二维力矩：沿着神经净层和沿时间方向水平的垂直力，这受益于有效近似的原始组合。我们对过渡内核的确定性近似适用于培训和预测。我们在多个实验中观察到，我们方法的不确定性校准质量只有在引入高计算成本后才通过蒙特卡洛采样来匹配。由于确定性培训的数值稳定性，我们的方法还提高了预测准确性。

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.

In this work, a method for obtaining pixel-wise error bounds in Bayesian regularization of inverse imaging problems is introduced. The proposed method employs estimates of the posterior variance together with techniques from conformal prediction in order to obtain coverage guarantees for the error bounds, without making any assumption on the underlying data distribution. It is generally applicable to Bayesian regularization approaches, independent, e.g., of the concrete choice of the prior. Furthermore, the coverage guarantees can also be obtained in case only approximate sampling from the posterior is possible. With this in particular, the proposed framework is able to incorporate any learned prior in a black-box manner. Guaranteed coverage without assumptions on the underlying distributions is only achievable since the magnitude of the error bounds is, in general, unknown in advance. Nevertheless, experiments with multiple regularization approaches presented in the paper confirm that in practice, the obtained error bounds are rather tight. For realizing the numerical experiments, also a novel primal-dual Langevin algorithm for sampling from non-smooth distributions is introduced in this work.