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.

Although weakly-supervised techniques can reduce the labeling effort, it is unclear whether a saliency model trained with weakly-supervised data (e.g., point annotation) can achieve the equivalent performance of its fully-supervised version. This paper attempts to answer this unexplored question by proving a hypothesis: there is a point-labeled dataset where saliency models trained on it can achieve equivalent performance when trained on the densely annotated dataset. To prove this conjecture, we proposed a novel yet effective adversarial trajectory-ensemble active learning (ATAL). Our contributions are three-fold: 1) Our proposed adversarial attack triggering uncertainty can conquer the overconfidence of existing active learning methods and accurately locate these uncertain pixels. {2)} Our proposed trajectory-ensemble uncertainty estimation method maintains the advantages of the ensemble networks while significantly reducing the computational cost. {3)} Our proposed relationship-aware diversity sampling algorithm can conquer oversampling while boosting performance. Experimental results show that our ATAL can find such a point-labeled dataset, where a saliency model trained on it obtained $97\%$ -- $99\%$ performance of its fully-supervised version with only ten annotated points per image.

For saving cost, many deep neural networks (DNNs) are trained on third-party datasets downloaded from internet, which enables attacker to implant backdoor into DNNs. In 2D domain, inherent structures of different image formats are similar. Hence, backdoor attack designed for one image format will suite for others. However, when it comes to 3D world, there is a huge disparity among different 3D data structures. As a result, backdoor pattern designed for one certain 3D data structure will be disable for other data structures of the same 3D scene. Therefore, this paper designs a uniform backdoor pattern: NRBdoor (Noisy Rotation Backdoor) which is able to adapt for heterogeneous 3D data structures. Specifically, we start from the unit rotation and then search for the optimal pattern by noise generation and selection process. The proposed NRBdoor is natural and imperceptible, since rotation is a common operation which usually contains noise due to both the miss match between a pair of points and the sensor calibration error for real-world 3D scene. Extensive experiments on 3D mesh and point cloud show that the proposed NRBdoor achieves state-of-the-art performance, with negligible shape variation.

Change detection (CD) is to decouple object changes (i.e., object missing or appearing) from background changes (i.e., environment variations) like light and season variations in two images captured in the same scene over a long time span, presenting critical applications in disaster management, urban development, etc. In particular, the endless patterns of background changes require detectors to have a high generalization against unseen environment variations, making this task significantly challenging. Recent deep learning-based methods develop novel network architectures or optimization strategies with paired-training examples, which do not handle the generalization issue explicitly and require huge manual pixel-level annotation efforts. In this work, for the first attempt in the CD community, we study the generalization issue of CD from the perspective of data augmentation and develop a novel weakly supervised training algorithm that only needs image-level labels. Different from general augmentation techniques for classification, we propose the background-mixed augmentation that is specifically designed for change detection by augmenting examples under the guidance of a set of background-changing images and letting deep CD models see diverse environment variations. Moreover, we propose the augmented & real data consistency loss that encourages the generalization increase significantly. Our method as a general framework can enhance a wide range of existing deep learning-based detectors. We conduct extensive experiments in two public datasets and enhance four state-of-the-art methods, demonstrating the advantages of our method. We release the code at https://github.com/tsingqguo/bgmix.

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.

持续学习（CL）依次学习像人类这样的新任务，其目标是实现更好的稳定性（S，记住过去的任务）和可塑性（P，适应新任务）。由于过去的培训数据不可用，因此探索培训示例中S和P的影响差异很有价值，这可能会改善对更好的SP的学习模式。受影响函数的启发（如果），我们首先研究了示例通过添加扰动来示例体重和计算影响推导的影响。为了避免在神经网络中Hessian逆的存储和计算负担，我们提出了一种简单而有效的METASP算法，以模拟IF计算中的两个关键步骤，并获得S-和P-Aware示例的影响。此外，我们建议通过解决双目标优化问题来融合两种示例影响，并获得对SP Pareto最优性的融合影响。融合影响可用于控制模型的更新并优化排练的存储。经验结果表明，我们的算法在任务和类别基准CL数据集上都显着优于最先进的方法。

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。

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。

隐肌通常会将覆盖媒体修改为嵌入秘密数据。最近出现了一种称为生成隐志（GS）的新型隐志方法，其中直接从秘密数据中生成了Stego图像（包含秘密数据的图像），而无需覆盖媒体。但是，现有的GS方案经常因其表现不佳而受到批评。在本文中，我们提出了一个先进的生成隐志网络（GSN），该网络可以在不使用封面图像的情况下生成逼真的Stego图像，其中首先在Stego Image生成中引入了相互信息。我们的模型包含四个子网络，即图像生成器（$ g $），一个歧视器（$ d $），steganalyzer（$ s $）和数据提取器（$ e $）。 $ d $和$ s $充当两个对抗歧视器，以确保生成的Stego图像的视觉和统计不可识别。 $ e $是从生成的Stego图像中提取隐藏的秘密。发电机$ g $灵活地构建以合成具有不同输入的封面或seego图像。它通过隐藏在普通图像发生器中生成seego图像的功能来促进秘密通信。一个名为Secret Block的模块设计用于在图像生成过程中掩盖特征地图中的秘密数据，并实现了高隐藏容量和图像保真度。此外，开发了一种新型的层次梯度衰减技能来抵抗切割分析的检测。实验证明了我们工作比现有方法的优越性。