The body of research on classification of solar panel arrays from aerial imagery is increasing, yet there are still not many public benchmark datasets. This paper introduces two novel benchmark datasets for classifying and localizing solar panel arrays in Denmark: A human annotated dataset for classification and segmentation, as well as a classification dataset acquired using self-reported data from the Danish national building registry. We explore the performance of prior works on the new benchmark dataset, and present results after fine-tuning models using a similar approach as recent works. Furthermore, we train models of newer architectures and provide benchmark baselines to our datasets in several scenarios. We believe the release of these datasets may improve future research in both local and global geospatial domains for identifying and mapping of solar panel arrays from aerial imagery. The data is accessible at https://osf.io/aj539/.

Masked Image Modelling (MIM) has been shown to be an efficient self-supervised learning (SSL) pre-training paradigm when paired with transformer architectures and in the presence of a large amount of unlabelled natural images. The combination of the difficulties in accessing and obtaining large amounts of labeled data and the availability of unlabelled data in the medical imaging domain makes MIM an interesting approach to advance deep learning (DL) applications based on 3D medical imaging data. Nevertheless, SSL and, in particular, MIM applications with medical imaging data are rather scarce and there is still uncertainty. around the potential of such a learning paradigm in the medical domain. We study MIM in the context of Prostate Cancer (PCa) lesion classification with T2 weighted (T2w) axial magnetic resonance imaging (MRI) data. In particular, we explore the effect of using MIM when coupled with convolutional neural networks (CNNs) under different conditions such as different masking strategies, obtaining better results in terms of AUC than other pre-training strategies like ImageNet weight initialization.

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.

Manually analyzing spermatozoa is a tremendous task for biologists due to the many fast-moving spermatozoa, causing inconsistencies in the quality of the assessments. Therefore, computer-assisted sperm analysis (CASA) has become a popular solution. Despite this, more data is needed to train supervised machine learning approaches in order to improve accuracy and reliability. In this regard, we provide a dataset called VISEM-Tracking with 20 video recordings of 30s of spermatozoa with manually annotated bounding-box coordinates and a set of sperm characteristics analyzed by experts in the domain. VISEM-Tracking is an extension of the previously published VISEM dataset. In addition to the annotated data, we provide unlabeled video clips for easy-to-use access and analysis of the data. As part of this paper, we present baseline sperm detection performances using the YOLOv5 deep learning model trained on the VISEM-Tracking dataset. As a result, the dataset can be used to train complex deep-learning models to analyze spermatozoa. The dataset is publicly available at https://zenodo.org/record/7293726.

作为估计高维网络的工具，图形模型通常应用于钙成像数据以估计功能性神经元连接，即神经元活动之间的关系。但是，在许多钙成像数据集中，没有同时记录整个神经元的人群，而是部分重叠的块。如（Vinci等人2019年）最初引入的，这导致了图形缝问题，在该问题中，目的是在仅观察到功能的子集时推断完整图的结构。在本文中，我们研究了一种新颖的两步方法来绘制缝的方法，该方法首先使用低级协方差完成技术在估计图结构之前使用低级协方差完成技术划分完整的协方差矩阵。我们介绍了三种解决此问题的方法：阻止奇异价值分解，核标准惩罚和非凸低级别分解。尽管先前的工作已经研究了低级别矩阵的完成，但我们解决了阻碍遗失的挑战，并且是第一个在图形学习背景下研究问题的挑战。我们讨论了两步过程的理论特性，通过证明新颖的l无限 - 基 - 误差界的矩阵完成，以块错失性证明了一种提出的方​​法的图选择一致性。然后，我们研究了所提出的方法在模拟和现实世界数据示例上的经验性能，通过该方法，我们显示了这些方法从钙成像数据中估算功能连通性的功效。

隐私已成为机器学习的主要问题。实际上，联合学习是出于隐私问题而激发的，因为它不允许传输私人数据，而仅传输中间更新。但是，联邦学习并不总是保证隐私保护，因为中间更新也可能揭示敏感信息。在本文中，我们对高斯混合模型的联合期望最大化算法进行了明确的信息理论分析，并证明了中间更新可能导致严重的隐私泄漏。为了解决隐私问题，我们提出了一个完全分散的隐私解决方案，该解决方案能够安全地计算每个最大化步骤中的更新。此外，我们考虑了两种不同类型的安全攻击：诚实但有趣而窃听的对手模型。数值验证表明，就准确性和隐私水平而言，与现有方法相比，所提出的方法具有优越的性能。

临床实践中使用的医学图像是异质的，与学术研究中研究的扫描质量不同。在解剖学，伪影或成像参数不寻常或方案不同的极端情况下，预处理会分解。最需要对这些变化的方法可靠。提出了一种新颖的深度学习方法，以将人脑快速分割为132个区域。提出的模型使用有效的U-NET型网络，并从不同视图和分层关系的交点上受益，以在端到端训练期间融合正交2D平面和脑标签。部署了弱监督的学习，以利用部分标记的数据来进行整个大脑分割和颅内体积（ICV）的估计。此外，数据增强用于通过生成具有较高的脑扫描的磁共振成像（MRI）数据来扩展模型训练，同时保持数据隐私。提出的方法可以应用于脑MRI数据，包括头骨或任何其他工件，而无需预处理图像或性能下降。与最新的一些实验相比，使用了不同的Atlases的几项实验，以评估受过训练模型的分割性能，并且与不同内部和不同内部和不同内部方法的现有方法相比，结果显示了较高的分割精度和鲁棒性。间域数据集。

全球抗菌耐药性（AMR）的增加是对人类健康的严重威胁。为了避免AMR的传播，快速可靠的诊断工具可以促进最佳的抗生素管理。在这方面，拉曼光谱学有望在一步中快速标记和无培养物鉴定以及抗菌敏感性测试（AST）。但是，尽管许多基于拉曼的细菌识别和AST研究表现出了令人印象深刻的结果，但仍必须解决一些缺点。为了弥合概念验证研究和临床应用之间的差距，我们与新的数据增强算法相结合开发了机器学习技术，以快速鉴定最小制备的细菌表型和甲氧西林抗甲氧西林（MR）的区别（MR）的区别甲氧西林敏感（MS）细菌。为此，我们为细菌的超光谱拉曼图像实施了光谱变压器模型。我们表明，我们的模型在精度和训练时间方面都超过了许多分类问题的标准卷积神经网络模型。对于六种MR-MS细菌物种，我们在数据集中达到了超过96美元的分类精度，该数据集由15个不同类别和95.6 $ \％$分类精度。更重要的是，我们的结果仅使用快速，易于生产的培训和测试数据获得

动物运动跟踪和姿势识别的进步一直是动物行为研究的游戏规则改变者。最近，越来越多的作品比跟踪“更深”，并解决了对动物内部状态（例如情绪和痛苦）的自动认识，目的是改善动物福利，这使得这是对该领域进行系统化的及时时刻。本文对基于计算机的识别情感状态和动物的疼痛的研究进行了全面调查，并涉及面部行为和身体行为分析。我们总结了迄今为止在这个主题中所付出的努力 - 对它们进行分类，从不同的维度进行分类，突出挑战和研究差距，并提供最佳实践建议，以推进该领域以及一些未来的研究方向。

We present a simple but novel hybrid approach to hyperspectral data cube reconstruction from computed tomography imaging spectrometry (CTIS) images that sequentially combines neural networks and the iterative Expectation Maximization (EM) algorithm. We train and test the ability of the method to reconstruct data cubes of $100\times100\times25$ and $100\times100\times100$ voxels, corresponding to 25 and 100 spectral channels, from simulated CTIS images generated by our CTIS simulator. The hybrid approach utilizes the inherent strength of the Convolutional Neural Network (CNN) with regard to noise and its ability to yield consistent reconstructions and make use of the EM algorithm's ability to generalize to spectral images of any object without training. The hybrid approach achieves better performance than both the CNNs and EM alone for seen (included in CNN training) and unseen (excluded from CNN training) cubes for both the 25- and 100-channel cases. For the 25 spectral channels, the improvements from CNN to the hybrid model (CNN + EM) in terms of the mean-squared errors are between 14-26%. For 100 spectral channels, the improvements between 19-40% are attained with the largest improvement of 40% for the unseen data, to which the CNNs are not exposed during the training.