Automated interpretation of medical images demands robust modeling of complex visual-semantic relationships while addressing annotation scarcity, label imbalance, and clinical plausibility constraints. We introduce MIRNet (Medical Image Reasoner Network), a novel framework that integrates self-supervised pre-training with constrained graph-based reasoning. Tongue image diagnosis is a particularly challenging domain that requires fine-grained visual and semantic understanding. Our approach leverages self-supervised masked autoencoder (MAE) to learn transferable visual representations from unlabeled data; employs graph attention networks (GAT) to model label correlations through expert-defined structured graphs; enforces clinical priors via constraint-aware optimization using KL divergence and regularization losses; and mitigates imbalance using asymmetric loss (ASL) and boosting ensembles. To address annotation scarcity, we also introduce TongueAtlas-4K, a comprehensive expert-curated benchmark comprising 4,000 images annotated with 22 diagnostic labels–representing the largest public dataset in tongue analysis. Validation shows our method achieves state-of-the-art performance. While optimized for tongue diagnosis, the framework readily generalizes to broader diagnostic medical imaging tasks.

To address the high complexity and noise sensitivity of the Temporal Multiple Sparse Bayesian Learning (TMSBL) algorithm in shallow-sea environments, this paper proposes a novel and robust channel estimation scheme. Our proposed scheme first denoises the received pilot matrix using the K-Singular Value Decomposition (KSVD) algorithm and then leverages the Stagewise Orthogonal Matching Pursuit (StOMP) to acquire a robust sparse prior to initializing the TMSBL framework. This structured approach leverages the temporal correlation between channels for joint estimation, while noise variance is estimated directly from OFDM null subcarriers to enhance stability and efficiency. The simulation results demonstrate the superiority of the proposed method. At an SNR of -10 dB, it reduces the normalized mean square error (NMSE) by more than 94% compared to the standard TMSBL algorithm and reduces the computation time by approximately 95.87%, ensuring higher accuracy and efficiency for underwater acoustic communications.

Black-box attacks pose a significant challenge due to the restricted access to target model information, hindering the generation of impactful adversarial samples. This paper introduces a method that combines sparse attacks and meta-learning to alleviate the issue of low success rates in black-box attacks. SAM leverages the knowledge transfer capabilities inherent in meta-learning to augment the transferability of adversarial samples. The method integrates meta-learning with gradient-based attack techniques, effectively transforming the approach into a white-box attack. By aggregating multiple sampled models, SAM enhances the stability of adversarial samples. During meta-testing, simulated black-box attacks help mitigate gradient discrepancies across diverse models, consequently enhancing transferability. To further improve sparsity and preserve transferability, SAM incorporates a projection strategy that selectively sparsifies global adversarial perturbations. Experimental evaluations conducted on two image datasets substantiate SAM’s superiority in terms of both sparsity and attack success rate. Ablation experiments confirm the effectiveness of integrating meta-learning into the proposed method. SAM extends the applicability of generated adversarial samples, advancing the domain of adversarial attacks in scenarios with limited target model information. The proposed approach exhibits promise in enhancing the success rate of attacks while preserving sparsity, contributing to the broader understanding of black-box attacks and their implications.