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.

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.

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.