Meniscal injury represents a common type of knee injury, accounting for over 50% of all knee injuries. The clinical diagnosis and treatment of meniscal injury heavily rely on magnetic resonance imaging (MRI). However, accurately diagnosing the meniscus from a comprehensive knee MRI is challenging due to its limited and weak signal, significantly impeding the precise grading of meniscal injuries. In this study, a visual interpretable fine grading (VIFG) diagnosis model has been developed to facilitate intelligent and quantified grading of meniscal injuries. Leveraging a multilevel transfer learning framework, it extracts comprehensive features and incorporates an attributional attention module to precisely locate the injured positions. Moreover, the attention-enhancing feedback module effectively concentrates on and distinguishes regions with similar grades of injury. The proposed method underwent validation on FastMRI_Knee and Xijing_Knee dataset, achieving mean grading accuracies of 0.8631 and 0.8502, surpassing the state-of-the-art grading methods notably in error-prone Grade 1 and Grade 2 cases. Additionally, the visually interpretable heatmaps generated by VIFG provide accurate depictions of actual or potential meniscus injury areas beyond human visual capability. Building upon this, a novel fine grading criterion was introduced for subtypes of meniscal injury, further classifying Grade 2 into 2a, 2b, and 2c, aligning with the anatomical knowledge of meniscal blood supply. It can provide enhanced injury-specific details, facilitating the development of more precise surgical strategies. The efficacy of this subtype classification was evidenced in 20 arthroscopic cases, underscoring the potential enhancement brought by intelligent-assisted diagnosis and treatment for meniscal injuries.

In synthetic aperture radar (SAR) image change detection, it is quite challenging to exploit the changing information from the noisy difference image subject to the speckle. In this paper, we propose a multi-scale spatial pooling (MSSP) network to exploit the changed information from the noisy difference image. Being different from the traditional convolutional network with only mono-scale pooling kernels, in the proposed method, multi-scale pooling kernels are equipped in a convolutional network to exploit the spatial context information on changed regions from the difference image. Furthermore, to verify the generalization of the proposed method, we apply our proposed method to the cross-dataset bitemporal SAR image change detection, where the MSSP network (MSSP-Net) is trained on a dataset and then applied to an unknown testing dataset. We compare the proposed method with other state-of-arts and the comparisons are performed on four challenging datasets of bitemporal SAR images. Experimental results demonstrate that our proposed method obtain comparable results with S-PCA-Net on YR-A and YR-B dataset and outperforms other state-of-art methods, especially on the Sendai-A and Sendai-B datasets with more complex scenes. More important, MSSP-Net is more efficient than S-PCA-Net and convolutional neural networks (CNN) with less executing time in both training and testing phases.

Link prediction in complex networks aims at predicting the missing links from available datasets which are always incomplete and subject to interfering noises. To obtain high prediction accuracy one should try to complete the missing information and at the same time eliminate the interfering noise from the datasets. Given that the global topological information of the networks can be exploited by the adjacent matrix, the missing information can be completed by generalizing the observed structure according to some consistency rule, and the noise can be eliminated by some proper decomposition techniques. Recently, two related works have been done that focused on each of the individual aspect and obtained satisfactory performances. Motivated by their complementary nature, here we proposed a new link prediction method that combines them together. Moreover, by extracting the symmetric part of the adjacent matrix, we also generalized the original perturbation method and extended our new method to weighted directed networks. Experimental studies on real networks from disparate fields indicate that the prediction accuracy of our method was considerably improved compared with either of the individual method as well as some other typical local indices.

Product theory of stochastic matrices provides a powerful tool in the consensus analysis of discrete-time multiagent systems. However, the classic theory cannot deal with networks with general coupling coefficients involving negative ones, which have been discussed only in very few papers due to the technicalities involved. Motivated by these works, here we developed some new results for the products of matrices which generalize that of the classical stochastic matrices by admitting negative entries. Particularly, we obtained a generalized version of the classic Hajnal inequality on this generalized matrix class. Based on these results, we proved some convergence results for a class of discrete-time consensus algorithms with time-varying delays and general coupling coefficients. At last, these results were applied to the analysis of a class of continuous-time consensus algorithms with discrete-time controller updates in the existence of communication/actuation delays.

Deep learning (DL) is widely applied in the field of hyperspectral image (HSI) classification and has proved to be an extremely promising research technique. However, the deployment of DL-based HSI classification algorithms in mobile and embedded vision applications tends to be limited by massive parameters, high memory costs, and the complex networks of DL models. In this article, we propose a novel, extremely lightweight, non-deep parallel network (HyperLiteNet) to address these issues. Based on the development trends of hardware devices, the proposed HyperLiteNet replaces the deep network by the parallel structure in terms of fewer sequential computations and lower latency. The parallel structure can extract and optimize the diverse and divergent spatial and spectral features independently. Meanwhile, an elaborately designed feature-interaction module is constructed to acquire and fuse generalized abstract spectral and spatial features in different parallel layers. The lightweight dynamic convolution further compresses the memory of the network to realize flexible spatial feature extraction. Experiments on several real HSI datasets confirm that the proposed HyperLiteNet can efficiently decrease the number of parameters and the execution time as well as achieve better classification performance compared to several recent state-of-the-art algorithms.

Accurate fine-grained grading of lumbar intervertebral disc (LIVD) degeneration is essential for the diagnosis and treatment design of high-incidence low back pain. However, the grading accuracy is still challenged by lacking the fine-grained degenerative details, which is mainly due to the existing grading methods are easily dominated by the salient nucleus pulposus regions in LIVD, overlooking the inconspicuous degeneration changes of the surrounding structures. In this study, a novel regional feature recalibration network (RFRecNet) is proposed to achieve accurate and reliable LIVD degeneration grading. Detection transformer (DETR) is first utilized to detect all LIVDs and then input to the proposed RFRecNet for the fine-grained grading. To obtain sufficient features from both the salient nucleus pulposus and the surrounding regions, a regional cube-based feature boosting and suppression (RC-FBS) module is designed to adaptively recalibrate the feature extraction and utilization from the various regions in LIVD, and a feature diversification (FD) module is proposed to capture the complementary semantic information from the multi-scale features for the comprehensive fine-grained degeneration grading. Extensive experiments were conducted on a clinically collected dataset, which consists of 500 MR scans with a total of 10225 LIVDs. An average grading accuracy of 90.5%, specificity of 97.5%, sensitivity of 90.8%, and Cohen's kappa correlation coefficient of 0.876 are obtained, which indicate that the proposed framework is promising to provide doctors with reliable and consistent fine-grained quantitative evaluation results of the LIVD degeneration conditions for the optimal surgical plan design.