纵向胎儿脑图集是理解和表征胎儿脑发育过程的复杂过程的强大工具。现有的胎儿脑图通常由离散时间点上的平均大脑图像构建，随着时间的流逝。由于样品在不同时间点的样品之间的遗传趋势差异，因此所得的地图遇到了时间不一致，这可能导致估计时间轴脑发育特征参数的误差。为此，我们提出了一个多阶段深度学习框架，以解决时间不一致问题作为4D（3D大脑体积 + 1D年龄）图像数据剥夺任务。使用隐式神经表示，我们构建了一个连续无噪声的纵向胎儿脑图集，这是4D时空坐标的函数。对两个公共胎儿脑图集（CRL和FBA-中心地图酶）的实验结果表明，所提出的方法可以显着提高Atlas时间一致性，同时保持良好的胎儿脑结构表示。另外，连续的纵向胎儿大脑图石也可以广泛应用于在空间和时间分辨率中生成更精细的4D图谱。

本文介绍了全髋关节置换术（THA）的手术机器人系统的开发和实验评估。尽管在关节置换手术中使用的现有机器人系统已经取得了一些进展，但机器人组必须在操作过程中准确地位于目标位置，这在很大程度上取决于外科医生的经验。此外，手持式髋臼铰刀通常表现出不均匀的强度和研磨文件。此外，缺乏实时测量股骨颈长度的技术可能导致结果不佳。为了应对这些挑战，我们提出了一种可实时可追溯的光学定位策略，以减少手术期间对机器人臂的不必要的手动调整，一种稳定磨削的最终效用系统，以及提供股骨颈的实时测量的光学探测器长度和其他参数用于选择适当的假体。下肢的长度在安装假体时测量。实验评估结果表明，根据其准确性，执行能力和鲁棒性，提出的手术机器人系统对于THA是可行的。

Supervised Deep-Learning (DL)-based reconstruction algorithms have shown state-of-the-art results for highly-undersampled dynamic Magnetic Resonance Imaging (MRI) reconstruction. However, the requirement of excessive high-quality ground-truth data hinders their applications due to the generalization problem. Recently, Implicit Neural Representation (INR) has appeared as a powerful DL-based tool for solving the inverse problem by characterizing the attributes of a signal as a continuous function of corresponding coordinates in an unsupervised manner. In this work, we proposed an INR-based method to improve dynamic MRI reconstruction from highly undersampled k-space data, which only takes spatiotemporal coordinates as inputs. Specifically, the proposed INR represents the dynamic MRI images as an implicit function and encodes them into neural networks. The weights of the network are learned from sparsely-acquired (k, t)-space data itself only, without external training datasets or prior images. Benefiting from the strong implicit continuity regularization of INR together with explicit regularization for low-rankness and sparsity, our proposed method outperforms the compared scan-specific methods at various acceleration factors. E.g., experiments on retrospective cardiac cine datasets show an improvement of 5.5 ~ 7.1 dB in PSNR for extremely high accelerations (up to 41.6-fold). The high-quality and inner continuity of the images provided by INR has great potential to further improve the spatiotemporal resolution of dynamic MRI, without the need of any training data.

Neural Radiance Field (NeRF) has widely received attention in Sparse-View Computed Tomography (SVCT) reconstruction tasks as a self-supervised deep learning framework. NeRF-based SVCT methods represent the desired CT image as a continuous function of spatial coordinates and train a Multi-Layer Perceptron (MLP) to learn the function by minimizing loss on the SV sinogram. Benefiting from the continuous representation provided by NeRF, the high-quality CT image can be reconstructed. However, existing NeRF-based SVCT methods strictly suppose there is completely no relative motion during the CT acquisition because they require \textit{accurate} projection poses to model the X-rays that scan the SV sinogram. Therefore, these methods suffer from severe performance drops for real SVCT imaging with motion. In this work, we propose a self-calibrating neural field to recover the artifacts-free image from the rigid motion-corrupted SV sinogram without using any external data. Specifically, we parametrize the inaccurate projection poses caused by rigid motion as trainable variables and then jointly optimize these pose variables and the MLP. We conduct numerical experiments on a public CT image dataset. The results indicate our model significantly outperforms two representative NeRF-based methods for SVCT reconstruction tasks with four different levels of rigid motion.

荧光显微镜是促进生物医学研究发现的关键驱动力。但是，随着显微镜硬件的局限性和观察到的样品的特征，荧光显微镜图像易受噪声。最近，已经提出了一些自我监督的深度学习（DL）denoising方法。但是，现有方法的训练效率和降解性能在实际场景噪声中相对较低。为了解决这个问题，本文提出了自我监督的图像denoising方法噪声2SR（N2SR），以训练基于单个嘈杂观察的简单有效的图像Denoising模型。我们的noings2SR Denoising模型设计用于使用不同维度的配对嘈杂图像进行训练。从这种训练策略中受益，Noige2SR更有效地自我监督，能够从单个嘈杂的观察结果中恢复更多图像细节。模拟噪声和真实显微镜噪声的实验结果表明，噪声2SR优于两个基于盲点的自我监督深度学习图像Denoising方法。我们设想噪声2SR有可能提高更多其他类型的科学成像质量。

在目前的工作中，我们提出了一个自制的坐标投影网络（范围），以通过解决逆断层扫描成像问题来从单个SV正弦图中重建无伪像的CT图像。与使用隐式神经代表网络（INR）解决类似问题的最新相关工作相比，我们的基本贡献是一种有效而简单的重新注射策略，可以将层析成像图像重建质量推向监督的深度学习CT重建工作。提出的策略是受线性代数与反问题之间的简单关系的启发。为了求解未确定的线性方程式系统，我们首先引入INR以通过图像连续性之前限制解决方案空间并实现粗糙解决方案。其次，我们建议生成一个密集的视图正式图，以改善线性方程系统的等级并产生更稳定的CT图像解决方案空间。我们的实验结果表明，重新投影策略显着提高了图像重建质量（至少为PSNR的+3 dB）。此外，我们将最近的哈希编码集成到我们的范围模型中，这极大地加速了模型培训。最后，我们评估并联和风扇X射线梁SVCT重建任务的范围。实验结果表明，所提出的范围模型优于两种基于INR的方法和两种受欢迎的监督DL方法。

具有深层神经网络的图像分类使技术突破激增，在面部识别，医学成像和自动驾驶等领域具有有希望的应用。然而，在工程问题中，例如发动机喷油器喷雾剂或身体油漆喷雾剂的高速成像，深度神经网络面临着与充足和多样性数据的可用性有关的根本挑战。通常，只有数千甚至数百个样本可供培训。此外，不同喷雾类之间的过渡是连续体，需要高水平的域专业知识来准确标记图像。在这项工作中，我们使用混音作为一种系统地处理工业喷雾应用中发现的数据稀缺和模棱两可的类界限的方法。我们表明，数据增强可以减轻小型数据集上大型神经网络的过度问题，但无法从根本上解决该问题。我们讨论了不同类别的凸线性插值如何自然与应用程序中不同类别之间的连续过渡保持一致。我们的实验表明，混合是一种简单而有效的方法，可以用仅几百个样品训练准确，坚固的深神网络分类器。

基于方面的情绪分析（ABSA）任务由三个典型的子特点组成：术语术语提取，意见术语提取和情感极性分类。这三个子组织通常是共同执行的，以节省资源并减少管道中的错误传播。但是，大多数现有联合模型只关注编码器共享的福利在子任务之间共享，但忽略差异。因此，我们提出了一个关节ABSA模型，它不仅享有编码器共享的好处，而且还专注于提高模型效率的差异。详细地，我们介绍了双编码器设计，其中一对编码器特别侧重于候选方识对分类，并且原始编码器对序列标记进行注意。经验结果表明，我们的拟议模型显示了鲁棒性，并显着优于前一个基准数据集的先前最先进。

High Resolution (HR) medical images provide rich anatomical structure details to facilitate early and accurate diagnosis. In MRI, restricted by hardware capacity, scan time, and patient cooperation ability, isotropic 3D HR image acquisition typically requests long scan time and, results in small spatial coverage and low SNR. Recent studies showed that, with deep convolutional neural networks, isotropic HR MR images could be recovered from low-resolution (LR) input via single image super-resolution (SISR) algorithms. However, most existing SISR methods tend to approach a scale-specific projection between LR and HR images, thus these methods can only deal with a fixed up-sampling rate. For achieving different up-sampling rates, multiple SR networks have to be built up respectively, which is very time-consuming and resource-intensive. In this paper, we propose ArSSR, an Arbitrary Scale Super-Resolution approach for recovering 3D HR MR images. In the ArSSR model, the reconstruction of HR images with different up-scaling rates is defined as learning a continuous implicit voxel function from the observed LR images. Then the SR task is converted to represent the implicit voxel function via deep neural networks from a set of paired HR-LR training examples. The ArSSR model consists of an encoder network and a decoder network. Specifically, the convolutional encoder network is to extract feature maps from the LR input images and the fully-connected decoder network is to approximate the implicit voxel function. Due to the continuity of the learned function, a single ArSSR model can achieve arbitrary up-sampling rate reconstruction of HR images from any input LR image after training. Experimental results on three datasets show that the ArSSR model can achieve state-of-the-art SR performance for 3D HR MR image reconstruction while using a single trained model to achieve arbitrary up-sampling scales.

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.