Unsupervised image registration commonly adopts U-Net style networks to predict dense displacement fields in the full-resolution spatial domain. For high-resolution volumetric image data, this process is however resource intensive and time-consuming. To tackle this problem, we propose the Fourier-Net, replacing the expansive path in a U-Net style network with a parameter-free model-driven decoder. Specifically, instead of our Fourier-Net learning to output a full-resolution displacement field in the spatial domain, we learn its low-dimensional representation in a band-limited Fourier domain. This representation is then decoded by our devised model-driven decoder (consisting of a zero padding layer and an inverse discrete Fourier transform layer) to the dense, full-resolution displacement field in the spatial domain. These changes allow our unsupervised Fourier-Net to contain fewer parameters and computational operations, resulting in faster inference speeds. Fourier-Net is then evaluated on two public 3D brain datasets against various state-of-the-art approaches. For example, when compared to a recent transformer-based method, i.e., TransMorph, our Fourier-Net, only using 0.22$\%$ of its parameters and 6.66$\%$ of the mult-adds, achieves a 0.6\% higher Dice score and an 11.48$\times$ faster inference speed. Code is available at \url{https://github.com/xi-jia/Fourier-Net}.

基于步态阶段的控制是步行AID机器人的热门研究主题，尤其是机器人下限假体。步态阶段估计是基于步态阶段控制的挑战。先前的研究使用了人类大腿角的整合或差异来估计步态阶段，但是累积的测量误差和噪声可能会影响估计结果。在本文中，提出了一种更健壮的步态相估计方法，使用各种运动模式的分段单调步态相位大角模型的统一形式。步态相仅根据大腿角度估算，这是一个稳定的变量，避免了相位漂移。基于卡尔曼滤波器的平滑液旨在进一步抑制估计步态阶段的突变。基于提出的步态相估计方法，基于步态阶段的关节角跟踪控制器是为跨股骨假体设计的。提出的步态估计方法，步态相和控制器通过在各种运动模式下的步行数据进行离线分析来评估。基于步态阶段的控制器的实时性能在经际假体的实验中得到了验证。

Focusing on the complicated pathological features, such as blurred boundaries, severe scale differences between symptoms, background noise interference, etc., in the task of retinal edema lesions joint segmentation from OCT images and enabling the segmentation results more reliable. In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network, which can provide accurate segmentation results with reliability assessment. Specifically, aiming at improving the model's ability to learn the complex pathological features of retinal edema lesions in OCT images, we develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module of our newly designed. Meanwhile, to make the segmentation results more reliable, a novel uncertainty segmentation head based on the subjective logical evidential theory is introduced to generate the final segmentation results with a corresponding overall uncertainty evaluation score map. We conduct comprehensive experiments on the public database of AI-Challenge 2018 for retinal edema lesions segmentation, and the results show that our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches. The code will be released on: https://github.com/LooKing9218/ReliableRESeg.

Localizing anatomical landmarks are important tasks in medical image analysis. However, the landmarks to be localized often lack prominent visual features. Their locations are elusive and easily confused with the background, and thus precise localization highly depends on the context formed by their surrounding areas. In addition, the required precision is usually higher than segmentation and object detection tasks. Therefore, localization has its unique challenges different from segmentation or detection. In this paper, we propose a zoom-in attentive network (ZIAN) for anatomical landmark localization in ocular images. First, a coarse-to-fine, or "zoom-in" strategy is utilized to learn the contextualized features in different scales. Then, an attentive fusion module is adopted to aggregate multi-scale features, which consists of 1) a co-attention network with a multiple regions-of-interest (ROIs) scheme that learns complementary features from the multiple ROIs, 2) an attention-based fusion module which integrates the multi-ROIs features and non-ROI features. We evaluated ZIAN on two open challenge tasks, i.e., the fovea localization in fundus images and scleral spur localization in AS-OCT images. Experiments show that ZIAN achieves promising performances and outperforms state-of-the-art localization methods. The source code and trained models of ZIAN are available at https://github.com/leixiaofeng-astar/OMIA9-ZIAN.

昼夜节律的破坏是阿尔茨海默氏病（AD）患者的基本症状。人类脑中基因表达的完整昼夜节律编排及其与AD的固有关联仍然很大程度上是未知的。我们提出了一种新颖的综合方法，即Prime，以检测和分析在多个数据集中不合时宜的高维基因表达数据中的节奏振荡模式。为了证明Prime的实用性，首先，我们通过从小鼠肝脏中的时间课程表达数据集作为跨物种和跨器官验证来对其进行验证。然后，我们将其应用于研究来自19个对照和AD患者的19个人脑区域的未接收基因组基因表达中的振荡模式。我们的发现揭示了15对控制大脑区域中清晰，同步的振荡模式，而这些振荡模式要么消失或昏暗。值得注意的是，Prime在不需要样品的时间戳而发现昼夜节律的节奏模式。 Prime的代码以及在本文中复制数字的代码，可在https://github.com/xinxingwu-uk/prime上获得。

在图像识别中已广泛提出了生成模型，以生成更多图像，其中分布与真实图像相似。它通常会引入一个歧视网络，以区分真实数据与生成的数据。这样的模型利用了一个歧视网络，该网络负责以区分样式从目标数据集中包含的数据传输的数据。但是，这样做的网络着重于强度分布的差异，并可能忽略数据集之间的结构差异。在本文中，我们制定了一个新的图像到图像翻译问题，以确保生成的图像的结构类似于目标数据集中的图像。我们提出了一个简单但功能强大的结构不稳定的对抗（SUA）网络，该网络在执行图像分割时介绍了训练和测试集之间的强度和结构差异。它由空间变换块组成，然后是强度分布渲染模块。提出了空间变换块来减少两个图像之间的结构缝隙，还产生了一个反变形字段，以使最终的分段图像背部扭曲。然后，强度分布渲染模块将变形结构呈现到具有目标强度分布的图像。实验结果表明，所提出的SUA方法具有在多个数据集之间传递强度分布和结构含量的能力。

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge -- Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application.

Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both two modalities of images have prominent biomarkers to indicate glaucoma suspected. Clinically, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment. Inspired by the success of Retinal Fundus Glaucoma Challenge (REFUGE) we held previously, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus \& OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus color photography and 3D OCT volumes, which is the first multi-modality dataset for glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, top-10 teams were selected to the final stage. We analysis their results and summarize their methods in the paper. Since all these teams submitted their source code in the challenge, a detailed ablation study is also conducted to verify the effectiveness of the particular modules proposed. We find many of the proposed techniques are practical for the clinical diagnosis of glaucoma. As the first in-depth study of fundus \& OCT multi-modality glaucoma grading, we believe the GAMMA Challenge will be an essential starting point for future research.

计算药物重新定位技术是加速药物开发的有效工具。虽然近几十年来这种技术已被广泛使用和成功，但许多现有模型仍然遭受多种缺点，例如矩阵分解模型中的大量未经验证的药物疾病关联和内部产品。这些作品的局限性主要是由于以下两个原因：首先，以前的作品使用负面采样技术将未经验证的毒性疾病关联视为负样本，在现实世界中无效;其次，内部产品缺乏对潜在因子的尺寸之间的交叉信息的建模。在本文中，我们提出了一种用于解决上述缺陷的小说框架，其模拟使用验证和未经验证的毒品疾病关联的毒性疾病关联的联合分布，而无需采用负面采样技术。 PUOS还使用外部产品操作建模了药物和疾病潜在因子的交叉信息。为了全面的比较，我们考虑了7个普遍的基线。两个现实世界数据集中的广泛实验表明，基于6个流行评估指标取得了最佳性能。

计算药物重新定位旨在发现销售药物的新治疗疾病，与传统药物开发相比，成本低，开发周期较低，可控性高的优点。由于其易于实现和优异的可扩展性，矩阵分解模型已成为计算药物重新定位的基石技术。然而，矩阵分解模型使用内在产品来表示药物和疾病之间的关联，这缺乏表达能力。此外，在其各自的潜在因子载体上不能暗示药物或疾病的相似性，这不满足常见的药物发现感。因此，在这项工作中提出了用于计算药物重新定位的神经度量分解模型（NMF）。我们新建了药物和疾病的潜在因子载体作为高维坐标系的点，提出了一种广义的欧氏距离，以代表药物和疾病之间的关联，以补偿内部产品的缺点。此外，通过将多种药物（疾病）指标信息嵌入到潜在因子向量的编码空间中，可以反映出药物（疾病）之间的相似性的信息反映在潜在因子向量之间的距离中。最后，我们对两个真实数据集进行了广泛的分析实验，以证明上述改进点和NMF模型的优越性的有效性。