Recent advances in AI-computing networks (ACN) provide a timely backdrop for assessing deep-learning (DL) research in healthcare. This systematic review synthesizes DL applications in multiple sclerosis (MS) and evaluates their readiness for ACN-enabled deployment. A Web of Science Core Collection search (2014-2024) retrieved 438 records; 264 met stringent inclusion criteria. Bibliometric and knowledge-mapping analyses reveal steady growth in publications and citations, with MRIbased UNet variants dominating lesion-segmentation and diseaseclassification tasks. Emerging themes include gait-sensor analytics, longitudinal progression modelling, quantitative susceptibility mapping, and the growing use of transfer and federated learning to overcome data scarcity and privacy barriers. These resourceaware strategies signal a shift toward distributed training and inference paradigms that align naturally with ACN architectures. Nevertheless, few studies report multi-institutional experiments or network-level performance metrics, underscoring the need for tighter integration between DL methods and ACN infrastructure. We highlight research gaps-particularly in cross-site model orchestration and low-latency, on-device inference-that AIcomputing networks are well positioned to address, enabling scalable and interoperable DL services for MS diagnosis and prognosis.

Guided depth super-resolution (GDSR) aims to enhance the level of detail in low-resolution depth images by utilizing the information present in the corresponding high-resolution RGB images. While existing methods utilize different approaches to guide the RGB image to the source image, they often ignore the texture similarity between these two images and usually suffer from unsatisfactory outline reconstruction of the depth map. In this article, we introduce an adaptive texture migration network (ATMNet) designed to mine rich feature information from RGB images and migrate them to the depth image. Specifically, we propose a multi-modal feature extractor (MMFE) to extract private and shared features between the depth map and RGB image. In addition, we present a texture migration module (TMM) to remap and fuse the features extracted from the raw image pairs. Last but not least, we develop a weighted adaptive loss to enhance the reconstruction of the edge areas in the depth map. Extensive experiments on public datasets such as Middlebury, NYUv2, and DIML demonstrate that our method outperforms the existing state-of-the-art GDSR methods and strikes a remarkable balance between performance and efficiency. The source code is available at https://github.com/MuggleTan/ATMNet .

Blind super-resolution (BSR) is entering a new era focused on diverse and complex applications, where the tradeoff between generalization and performance prevents models from performing as they should. Model performance decreases when trained on multiple degraded images due to the inter-class and intra-class imbalances in degradation prediction, which consists of degradation sampling and estimation. The inter-class imbalance in degradation estimation causes inaccurate estimates, leading to severe artifacts in images. The intra-class imbalance in degradation sampling causes a long-tail problem, leading to model collapse and satisfactory results only in specific applications. To tackle these challenges, we propose adaptive alignment contrastive learning (AACL), which includes adaptive degradation sampling (ADS) and \(\sigma\) -alignment. ADS utilizes non-linear sampling by weighting the parameters of the degradation process for training uniformly degraded images, avoiding the long-tail problem. \(\sigma\) -alignment controls the SD among positive samples; we identify a subset with small degraded distance, which aids contrastive learning in extracting representations more effectively. We extend AACL to several CNN-based and Transformer-based methods by coming up with a 6 \(\times\) 6 fair architecture with degradation representation fusion block (DRFB) and degradation representation fusion group (DRFG). DRFB and DRFG are designed for degradation representation fusion and image reconstruction, respectively. We evaluate on six types of degradation, and the improvement experiments on synthesized images show that our method balances performance and generalization and is applicable to networks with different architectures. The comparison experiments show that our improved methods achieve promising results compared to SOTA methods. Code is available at: https://github.com/para999/AACL .

Single domain generalization (single-DG) is a realistic yet challenging domain generalization scenario where a model trained on a single domain generalization scenario where a model trained on a single domain generalizes well to multiple unseen domains. Unlike typical single-DG methods that are essentially supervised data augmentation and focus mainly on the novelty of images, we propose a simple adversarial augmentation method, termed Progressive Diversity Generation (PDG), to synthesize novel and diverse images in a fully unsupervised manner. Specifically, PDG minimizes the uncertainty coefficient to ensure that synthesized images are novel. By modeling conditional probabilities with an auxiliary network, we transfer the adversarial process from semantics to images, thus eliminating dependency on labels. To enhance diversity, we propose the$f$-diversity, a collection of correlation or similarity measures, to allow our model to generate potential images from diverse perspectives. The proposed architecture combines a multi-attribute generator with a progressive generation framework to improve model performance. PDG is the unsupervised and easy-to-implement method that solves single-DG with only synthesized (source) images. Extensive experiments on multiple single-DG benchmarks show that PDG achieves remarkable results and outperforms existing supervised and unsupervised methods by a large margin in single domain generalization. Source code and data are available:https://github.com/Ruiding1/PDG.

Reference-based image super-resolution (RefSR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) images by introducing HR reference images. The key step of RefSR is to transfer reference features to LR features. However, existing methods still lack an efficient transfer mechanism, resulting in blurry details in the generated image. In this article, we propose a double-layer search module and an adaptive pooling fusion module group for reference-based image super-resolution, called DLASR. Based on the re-search strategy, the double-layer search module can produce an accurate index map and score map. These two maps are used to filter out accurate reference features, which greatly increases the efficiency of feature transfer in the later stage. Through two continuous feature-enhancement steps, the adaptive pooling fusion module group can transfer more valuable reference features to the corresponding LR features. In addition, a structure reconstruction module is proposed to recover the geometric information of the images, which further improves the visual quality of the generated image. We conduct comparative experiments on a variety of datasets, and the results prove that DLASR achieves significant improvements over other state-of-the-art methods, in terms of quantitative accuracy and qualitative visual effect. The code is available at https://github.com/clttyou/DLASR.

Scene text image super-resolution (STISR) in the wild has been shown to be beneficial to support improved vision-based text recognition from low-resolution imagery. An intuitive way to enhance STISR performance is to explore the well-structured and repetitive layout characteristics of text and exploit these as prior knowledge to guide model convergence. In this paper, we propose a novel gradient-based graph attention method to embed patch-wise text layout contexts into image feature representations for high-resolution text image reconstruction in an implicit and elegant manner. We introduce a non-local group-wise attention module to extract text features which are then enhanced by a cascaded channel attention module and a novel gradient-based graph attention module in order to obtain more effective representations by exploring correlations of regional and local patch-wise text layout properties. Extensive experiments on the benchmark TextZoom dataset convincingly demonstrate that our method supports excellent text recognition and outperforms the current state-of-the-art in STISR. The source code is available at https://github.com/xyzhu1/TSAN.

Single domain generalization (SDG) is a realistic yet challenging domain generalization scenario that aims to generalize a model trained on a single domain to multiple unseen domains. Typical SDG methods are essentially supervised data augmentation strategies, which tend to enhance the novelty rather than the diversity of augmented samples. Insufficient diversity may jeopardize the model generalization ability. In this paper, we propose a novel adversarial method, termed Unsupervised Diversity Probe (UDP), to synthesize novel and diverse samples in fully unsupervised settings. More specifically, to ensure that samples are novel, we study SDG from an information-theoretic perspective that minimizes the uncertainty coefficients between synthesized and source samples. Considering that the variation in a single source domain is limited, we introduce a regularization imposed on the auxiliary module that synthesizes variable samples, incorporated with uncertainty coefficients in an adversarial manner to complement the diversity. Subsequently, an available region is utilized to guarantee the samples' safety. For the network architecture, we design a simple probe module that can synthesize samples in several different aspects. UDP is an unsupervised and easy-to-implement method that solves SDG using only synthetic (source) samples, thus reducing the dependence on task models. Extensive experiments on three benchmark datasets show that UDP achieves remarkable results and outperforms existing supervised and unsupervised methods by a large margin in single domain generalization.

We propose ComGAN, a simple unsupervised generative model, which simultaneously generates realistic images and high semantic masks under an adversarial loss and a binary regularization. In this paper, we first investigate two kinds of trivial solutions in the compositional generation process, and demonstrate their source is vanishing gradients on the mask. Then, we solve trivial solutions from the perspective of architecture. Furthermore, we redesign two fully unsupervised modules based on ComGAN (DS-ComGAN), where the disentanglement module associates the foreground, background and mask with three independent variables, and the segmentation module learns object segmentation. Experimental results show that (i) ComGAN's network architecture effectively avoids trivial solutions without any supervised information and regularization; (ii) DS-ComGAN achieves remarkable results and outperforms existing semi-supervised and weakly supervised methods by a large margin in both the image disentanglement and unsupervised segmentation tasks. It implies that the redesign of ComGAN is a possible direction for future unsupervised work.

In recent years, with the rapid development of deep learning, super-resolution methods based on convolutional neural networks (CNNs) have made great progress. However, the parameters and the required consumption of computing resources of these methods are also increasing to the point that such methods are difficult to implement on devices with low computing power. To address this issue, we propose a lightweight single image super-resolution network with an expectation-maximization attention mechanism (EMASRN) for better balancing performance and applicability. Specifically, a progressive multi-scale feature extraction block (PMSFE) is proposed to extract feature maps of different sizes. Furthermore, we propose an HR-size expectation-maximization attention block (HREMAB) that directly captures the long-range dependencies of HR-size feature maps. We also utilize a feedback network to feed the high-level features of each generation into the next generation’s shallow network. Compared with the existing lightweight single image super-resolution (SISR) methods, our EMASRN reduces the number of parameters by almost one-third. The experimental results demonstrate the superiority of our EMASRN over state-of-the-art lightweight SISR methods in terms of both quantitative metrics and visual quality. The source code can be downloaded athttps://github.com/xyzhu1/EMASRN.

Stereo image super-resolution exploits additional features from cross view image pairs for high resolution (HR) image reconstruction. Recently, several new methods have been proposed to investigate cross view features along epipolar lines to enhance the visual perception of recovered HR images. Despite the impressive performance of these methods, global contextual features from cross view images are left unexplored. In this paper, we propose a cross view capture network (CVCnet) for stereo image super-resolution by using both global contextual and local features extracted from both views. Specifically, we design a cross view block to capture diverse feature embeddings from the views in stereo vision. In addition, a cascaded spatial perception module is proposed to redistribute each location in feature maps according to the weight it occupies to make the extraction of features more effective. Extensive experiments demonstrate that our proposed CVCnet outperforms the state-of-the-art image super-resolution methods to achieve the best performance for stereo image super-resolution tasks. The source code is available at https://github.com/xyzhu1/CVCnet.

Pairwise sequence alignment is a common and fundamental task in Computational Biology, which constitutes the basis for many Bioinformatics applications. In the post-genomic era, there is an increasing demand to align long DNA sequences to discover their functions. In this paper, we propose a parallel pairwise alignment algorithm for large genomic sequences by recursively dividing the whole genomic sequences into small pieces, with an effective pruning strategy to reduce search and computation space. We implemented rigorous tests on a 4-core computer using real genomic sequences and artificially generated sequences. The results show that our implementation can achieve speedup 10.64 with 99.75% accuracy compared to the sequential algorithm. As far as we know, this is the first time that MBP (mega base-pairs) sequences are globally aligned with an affine gap penalty.

Multiple sequence alignment (MSA) constitutes an extremely powerful tool for many biological applications including phylogenetic tree estimation, secondary structure prediction, and critical residue identification. However, aligning large biological sequences with popular tools such as MAFFT requires long runtimes on sequential architectures. Due to the ever increasing sizes of sequence databases, there is increasing demand to accelerate this task. In this paper, we demonstrate how graphic processing units (GPUs), powered by the compute unified device architecture (CUDA), can be used as an efficient computational platform to accelerate the MAFFT algorithm. To fully exploit the GPU's capabilities for accelerating MAFFT, we have optimized the sequence data organization to eliminate the bandwidth bottleneck of memory access, designed a memory allocation and reuse strategy to make full use of limited memory of GPUs, proposed a new modified-run-length encoding (MRLE) scheme to reduce memory consumption, and used high-performance shared memory to speed up I/O operations. Our implementation tested in three NVIDIA GPUs achieves speedup up to 11.28 on a Tesla K20m GPU compared to the sequential MAFFT 7.015.

Multiple sequence alignment (MSA) constitutes an extremely powerful tool for many biological applications including phylogenetic tree estimation, secondary structure prediction, and critical residue identification. However, aligning large biological sequences with popular tools such as MAFFT requires long runtimes on sequential architectures. Due to the ever increasing sizes of sequence databases, there is increasing demand to accelerate this task. In this paper, we demonstrate how Graphic Processing Units (GPUs), powered by the Compute Unified Device Architecture (CUDA), can be used as an efficient computational platform to accelerate the MAFFT algorithm. To fully exploit the GPU's capabilities for accelerating MAFFT, we have optimized the sequence data organization to eliminate the bandwidth bottleneck of memory access, and designed a memory allocation and reuse strategy to make full use of limited memory of GPUs. Our implementation achieves speedup up to 19.58 and 4.14 on an NVIDIA Tesla C2050 GPU compared to the sequential and multi-thread MAFFT 7.017, respectively.