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。

Automatic Image Cropping is a challenging task with many practical downstream applications. The task is often divided into sub-problems - generating cropping candidates, finding the visually important regions, and determining aesthetics to select the most appealing candidate. Prior approaches model one or more of these sub-problems separately, and often combine them sequentially. We propose a novel convolutional neural network (CNN) based method to crop images directly, without explicitly modeling image aesthetics, evaluating multiple crop candidates, or detecting visually salient regions. Our model is trained on a large dataset of images cropped by experienced editors and can simultaneously predict bounding boxes for multiple fixed aspect ratios. We consider the aspect ratio of the cropped image to be a critical factor that influences aesthetics. Prior approaches for automatic image cropping, did not enforce the aspect ratio of the outputs, likely due to a lack of datasets for this task. We, therefore, benchmark our method on public datasets for two related tasks - first, aesthetic image cropping without regard to aspect ratio, and second, thumbnail generation that requires fixed aspect ratio outputs, but where aesthetics are not crucial. We show that our strategy is competitive with or performs better than existing methods in both these tasks. Furthermore, our one-stage model is easier to train and significantly faster than existing two-stage or end-to-end methods for inference. We present a qualitative evaluation study, and find that our model is able to generalize to diverse images from unseen datasets and often retains compositional properties of the original images after cropping. Our results demonstrate that explicitly modeling image aesthetics or visual attention regions is not necessarily required to build a competitive image cropping algorithm.

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.

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/.

Many scientific domains gather sufficient labels to train machine algorithms through human-in-the-loop techniques provided by the Zooniverse.org citizen science platform. As the range of projects, task types and data rates increase, acceleration of model training is of paramount concern to focus volunteer effort where most needed. The application of Transfer Learning (TL) between Zooniverse projects holds promise as a solution. However, understanding the effectiveness of TL approaches that pretrain on large-scale generic image sets vs. images with similar characteristics possibly from similar tasks is an open challenge. We apply a generative segmentation model on two Zooniverse project-based data sets: (1) to identify fat droplets in liver cells (FatChecker; FC) and (2) the identification of kelp beds in satellite images (Floating Forests; FF) through transfer learning from the first project. We compare and contrast its performance with a TL model based on the COCO image set, and subsequently with baseline counterparts. We find that both the FC and COCO TL models perform better than the baseline cases when using >75% of the original training sample size. The COCO-based TL model generally performs better than the FC-based one, likely due to its generalized features. Our investigations provide important insights into usage of TL approaches on multi-domain data hosted across different Zooniverse projects, enabling future projects to accelerate task completion.

Recent 3D-based manipulation methods either directly predict the grasp pose using 3D neural networks, or solve the grasp pose using similar objects retrieved from shape databases. However, the former faces generalizability challenges when testing with new robot arms or unseen objects; and the latter assumes that similar objects exist in the databases. We hypothesize that recent 3D modeling methods provides a path towards building digital replica of the evaluation scene that affords physical simulation and supports robust manipulation algorithm learning. We propose to reconstruct high-quality meshes from real-world point clouds using state-of-the-art neural surface reconstruction method (the Real2Sim step). Because most simulators take meshes for fast simulation, the reconstructed meshes enable grasp pose labels generation without human efforts. The generated labels can train grasp network that performs robustly in the real evaluation scene (the Sim2Real step). In synthetic and real experiments, we show that the Real2Sim2Real pipeline performs better than baseline grasp networks trained with a large dataset and a grasp sampling method with retrieval-based reconstruction. The benefit of the Real2Sim2Real pipeline comes from 1) decoupling scene modeling and grasp sampling into sub-problems, and 2) both sub-problems can be solved with sufficiently high quality using recent 3D learning algorithms and mesh-based physical simulation techniques.

医学图像分割模型的性能指标用于衡量参考注释和预测之间的一致性。在开发此类模型中，使用了一组通用指标，以使结果更具可比性。但是，公共数据集中的分布与临床实践中遇到的案例之间存在不匹配。许多常见的指标无法衡量这种不匹配的影响，尤其是对于包含不确定，小或空参考注释的临床数据集。因此，可能无法通过此类指标来验证模型在临床上有意义的一致性。评估临床价值的维度包括独立于参考注释量的大小，考虑参考注释的不确定性，体积计和/或位置一致性的奖励以及对空参考注释正确分类的奖励。与普通的公共数据集不同，我们的内部数据集更具代表性。它包含不确定的，小或空的参考注释。我们研究了有关深度学习框架的预测的公开度量指标，以确定哪些设置共同指标可提供有意义的结果。我们将公共基准数据集进行比较而没有不确定，小或空参考注释。该代码将发布。

噪声的去除或取消对成像和声学具有广泛的应用。在日常生活中，Denoising甚至可能包括对地面真理不忠的生成方面。但是，对于科学应用，denoing必须准确地重现地面真相。在这里，我们展示了如何通过深层卷积神经网络来定位数据，从而以定量精度出现弱信号。特别是，我们研究了晶体材料的X射线衍射。我们证明，弱信号是由电荷排序引起的，在嘈杂的数据中微不足道的信号，在DeNo的数据中变得可见和准确。通过对深度神经网络的监督培训，具有成对的低噪声数据，可以通过监督培训来实现这一成功。这样，神经网络就可以了解噪声的统计特性。我们证明，使用人造噪声（例如泊松和高斯）不会产生这种定量准确的结果。因此，我们的方法说明了一种实用的噪声过滤策略，可以应用于具有挑战性的获取问题。

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。

线虫秀丽隐杆线虫（秀丽隐杆线虫）被用作模型生物体，以更好地了解发育生物学和神经生物学。秀丽隐杆线虫具有不变的细胞谱系，已使用荧光显微镜图像进行了分类和观察。然而，一旦开始零星的肌肉抽搐，已建立的跟踪细胞的方法就无法概括。我们以方法为基础，该方法将皮肤细胞用作基准标记，尽管随机抽搐，但仍在进行细胞跟踪。特别是，我们提出了一个细胞核分割和跟踪程序，该过程被整合到3D渲染GUI中，以提高在晚期发育过程中跟踪细胞的效率。在三个测试胚胎上描述上述肌肉细胞核的图像上的结果表明，基准标记与经典的跟踪范式结合使用，克服了零星的抽搐。