For medical image segmentation, accurately balancing local details and global long-range dependencies is critical to tackling thyroid nodule challenges (variable sizes, ambiguous boundaries, complex context). Traditional CNNs excel at local feature extraction but are constrained by local receptive fields, hindering efficient global dependency modeling. To address this, we propose a Parallel Mamba Dual-U Network (PM-DUNet). It adopts a cascaded dual U-Net encoder-decoder for two-stage “coarse-to-fine” segmentation refinement. We design a Multi-Path Parallel Mamba (MPM) module—using State Space Models (SSMs)—to efficiently model global context with linear complexity. Additionally, Squeeze-Excitation Downsampling (SED) and Spatial Attention Upsampling (SAU) modules are integrated to adaptively enhance key features in encoding/decoding. Results show PM-DUNet achieves highly competitive performance and outperforms state-of-the-art methods on most core metrics, verifying its effectiveness and robustness for complex medical image segmentation. Our code is available on https://github.com/Andrevict/MPDUNet.
Shaoqiang Wang, Linhao Zhang, Guiling Shi et al.· PLoS ONE· 0 citations
The sharing of patient-level structured data is strictly constrained by privacy regulations and governance, creating “data silos” that hinder multi-center research. We propose the CLEO (Clean-Learn-Evaluate-Optimize), a closed-loop framework that integrates a Gaussian Mixture Model generator with Q-learning-based optimization to formalize data synthesis as a Markov Decision Process. Experimental results on a multicenter intracranial aneurysm dataset show that CLEO achieved a combined score of 0.9232 ± 0.0124, outperforming the evaluated representative comparison methods, including TVAE, Gaussian Copula, and CTGAN. In downstream TSTR evaluation, models trained on CLEO-generated data achieved an average AUC of 0.7376, indicating that the synthetic data retained useful clinical decision signals. However, Macro-F1 results also suggest that minority-class prediction remains affected by the imbalanced class distribution. Empirical privacy auditing showed a Nearest Neighbor Adversarial Accuracy score of 0.4940, suggesting low observed re-identification risk under the adopted nearest-neighbor audit setting. These findings indicate that CLEO provides a controllable and empirically auditable framework for supporting cross-institutional research when real-world medical data cannot be directly aggregated.
Siqi Wang, Jianfeng Wang, Xiaochun Cheng et al.· npj Digital Medicine· 0 citations