Unlike traditional distributed machine learning, federated learning stores data locally for training and then aggregates the models on the server, which solves the data security problem that may arise in traditional distributed machine learning. However, during the training process, the transmission of model parameters can impose a significant load on the network bandwidth. It has been pointed out that the vast majority of model parameters are redundant during model parameter transmission. In this paper, we explore the data distribution law of selected partial model parameters on this basis, and propose a deep hierarchical quantization compression algorithm, which further compresses the model and reduces the network load brought by data transmission through the hierarchical quantization of model parameters. And we adopt a dynamic sampling strategy for the selection of clients to accelerate the convergence of the model. Experimental results on different public datasets demonstrate the effectiveness of our algorithm.

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

经过嘈杂标签训练的深层模型很容易在概括中过度拟合和挣扎。大多数现有的解决方案都是基于理想的假设，即标签噪声是类条件，即同一类的实例共享相同的噪声模型，并且独立于特征。在实践中，现实世界中的噪声模式通常更为细粒度作为实例依赖性，这构成了巨大的挑战，尤其是在阶层间失衡的情况下。在本文中，我们提出了一种两阶段的干净样品识别方法，以应对上述挑战。首先，我们采用类级特征聚类程序，以早期识别在班级预测中心附近的干净样品。值得注意的是，我们根据稀有类的预测熵来解决类不平衡问题。其次，对于接近地面真相类边界的其余清洁样品（通常与样品与实例有关的噪声混合），我们提出了一种基于一致性的新型分类方法，该方法使用两个分类器头的一致性来识别它们：一致性越高，样品清洁的可能性就越大。对几个具有挑战性的基准进行了广泛的实验，证明了我们的方法与最先进的方法相比。

联合学习（FL）引发了高通信开销，这可以通过压缩模型更新而大大缓解。然而，网络环境中压缩和模型精度之间的权衡仍不清楚，为简单起见，大多数实现仅采用固定压缩率。在本文中，我们首次系统地检查了该权衡，识别压缩误差对最终模型精度的影响，相对于学习率。具体而言，我们将每个全局迭代的压缩误差因其强大凸面和非凸损耗下的收敛速度分析。然后，我们通过策略性地调整每次迭代中的压缩速率来提高最终模型精度来最大化最终模型精度的适应框架。我们讨论了具有代表压缩算法的实用网络中框架的关键实施问题。对流行的MNIST和CIFAR-10数据集的实验证实，我们的解决方案有效地降低了网络流量，但在FL中保持了高模型精度。

我们提出了一种小说的无参考质量评估度量，图像转移点云质量评估（IT-PCQA），用于3D点云。对于质量评估，深度神经网络（DNN）在无参考度量设计上显示了令人信服的性能。但是，无引用PCQA最具挑战性的问题是我们缺乏大规模的主观数据库来驱动强大的网络。我们的动机是人类视觉系统（HVS）是决策者，无论质量评估的媒体类型如何。利用自然图像的丰富主观评分，我们可以通过DNN探讨人类感知的评估标准，并将预测的能力转移到3D点云。特别是，我们将自然图像视为源域和点云作为目标域，并通过无监督的对抗域适应推断云质量。为了提取有效的潜在特征并最小化域差异，我们提出了分层特征编码器和条件鉴别网络。考虑到最终目的是回归客观评分，我们在条件鉴别网络中引入了一种新的条件跨熵损失，以惩罚阻碍质量回归网络的收敛的负样本。实验结果表明，该方法可以实现比传统的无参考度量更高的性能，甚至与全引用度量的相当结果。该方法还表明，在没有昂贵和繁琐的主观评估的情况下评估特定媒体内容质量的可行性。

Supervised machine learning-based medical image computing applications necessitate expert label curation, while unlabelled image data might be relatively abundant. Active learning methods aim to prioritise a subset of available image data for expert annotation, for label-efficient model training. We develop a controller neural network that measures priority of images in a sequence of batches, as in batch-mode active learning, for multi-class segmentation tasks. The controller is optimised by rewarding positive task-specific performance gain, within a Markov decision process (MDP) environment that also optimises the task predictor. In this work, the task predictor is a segmentation network. A meta-reinforcement learning algorithm is proposed with multiple MDPs, such that the pre-trained controller can be adapted to a new MDP that contains data from different institutes and/or requires segmentation of different organs or structures within the abdomen. We present experimental results using multiple CT datasets from more than one thousand patients, with segmentation tasks of nine different abdominal organs, to demonstrate the efficacy of the learnt prioritisation controller function and its cross-institute and cross-organ adaptability. We show that the proposed adaptable prioritisation metric yields converging segmentation accuracy for the novel class of kidney, unseen in training, using between approximately 40\% to 60\% of labels otherwise required with other heuristic or random prioritisation metrics. For clinical datasets of limited size, the proposed adaptable prioritisation offers a performance improvement of 22.6\% and 10.2\% in Dice score, for tasks of kidney and liver vessel segmentation, respectively, compared to random prioritisation and alternative active sampling strategies.

Recent studies show that, despite being effective on numerous tasks, text processing algorithms may be vulnerable to deliberate attacks. However, the question of whether such weaknesses can directly lead to security threats is still under-explored. To bridge this gap, we conducted vulnerability tests on Text-to-SQL, a technique that builds natural language interfaces for databases. Empirically, we showed that the Text-to-SQL modules of two commercial black boxes (Baidu-UNIT and Codex-powered Ai2sql) can be manipulated to produce malicious code, potentially leading to data breaches and Denial of Service. This is the first demonstration of the danger of NLP models being exploited as attack vectors in the wild. Moreover, experiments involving four open-source frameworks verified that simple backdoor attacks can achieve a 100% success rate on Text-to-SQL systems with almost no prediction performance impact. By reporting these findings and suggesting practical defences, we call for immediate attention from the NLP community to the identification and remediation of software security issues.

Three-dimensional (3D) freehand ultrasound (US) reconstruction without a tracker can be advantageous over its two-dimensional or tracked counterparts in many clinical applications. In this paper, we propose to estimate 3D spatial transformation between US frames from both past and future 2D images, using feed-forward and recurrent neural networks (RNNs). With the temporally available frames, a further multi-task learning algorithm is proposed to utilise a large number of auxiliary transformation-predicting tasks between them. Using more than 40,000 US frames acquired from 228 scans on 38 forearms of 19 volunteers in a volunteer study, the hold-out test performance is quantified by frame prediction accuracy, volume reconstruction overlap, accumulated tracking error and final drift, based on ground-truth from an optical tracker. The results show the importance of modelling the temporal-spatially correlated input frames as well as output transformations, with further improvement owing to additional past and/or future frames. The best performing model was associated with predicting transformation between moderately-spaced frames, with an interval of less than ten frames at 20 frames per second (fps). Little benefit was observed by adding frames more than one second away from the predicted transformation, with or without LSTM-based RNNs. Interestingly, with the proposed approach, explicit within-sequence loss that encourages consistency in composing transformations or minimises accumulated error may no longer be required. The implementation code and volunteer data will be made publicly available ensuring reproducibility and further research.

在域移位下，跨域几个射击对象检测旨在通过一些注释的目标数据适应目标域中的对象检测器。存在两个重大挑战：（1）高度不足的目标域数据； （2）潜在的过度适应和误导性是由不当放大的目标样本而没有任何限制引起的。为了应对这些挑战，我们提出了一种由两个部分组成的自适应方法。首先，我们提出了一种自适应优化策略，以选择类似于目标样本的增强数据，而不是盲目增加数量。具体而言，我们过滤了增强的候选者，这些候选者在一开始就显着偏离了目标特征分布。其次，为了进一步释放数据限制，我们提出了多级域感知数据增强，以增加增强数据的多样性和合理性，从而利用了跨图像前景 - 背景混合物。实验表明，所提出的方法在多个基准测试中实现了最先进的性能。

在医学图像分析中需要进行几次学习的能力是对支持图像数据的有效利用，该数据被标记为对新类进行分类或细分新类，该任务否则需要更多的培训图像和专家注释。这项工作描述了一种完全3D原型的几种分段算法，因此，训练有素的网络可以有效地适应培训中缺乏的临床有趣结构，仅使用来自不同研究所的几个标记图像。首先，为了弥补机构在新型类别的情节适应中的广泛认识的空间变异性，新型的空间注册机制被整合到原型学习中，由分割头和空间对齐模块组成。其次，为了帮助训练观察到的不完美比对，提出了支持掩模调节模块，以进一步利用支持图像中可用的注释。使用589个骨盆T2加权MR图像的数据集分割了八个对介入计划的解剖结构的应用，该实验是针对介入八个机构的八个解剖结构的应用。结果证明了3D公式中的每种，空间登记和支持掩模条件的功效，所有这些条件都独立或集体地做出了积极的贡献。与先前提出的2D替代方案相比，不管支持数据来自相同还是不同的机构，都具有统计学意义的少量分割性能。