Chunlin Yu

Medical image segmentation is a crucial task of medical image analysis and computer vision. Medical images, compared to natural ones, contain more complex semantic information, making feature learning more challenging. Existing encoder-decoder architectures are limited by inadequate cross-scale interaction and insufficient boundary modeling in their feature fusion designs. To address this, we propose a Hierarchical Feature Interaction network with Boundary guidance (HFIBNet), which unifies dynamic cross-level feature fusion and explicit edge supervision within a coarse-to-fine segmentation framework. Specifically, we introduce a Boundary Prediction (BP) module to extract boundary-aware features that guide the fusion process. A Cross-Level Feature Fusion (CLFF) module is designed to promote semantic interaction across adjacent encoder stages, while the Edge Feature Aggregation (EFA) module propagates boundary cues hierarchically to enhance structural consistency. Furthermore, a Partially Parallel Decoder (PPD) generates a coarse global prediction, which is progressively refined by a Global-Local Feature Enrichment (GLFE) module, mimicking the clinical annotation workflow from coarse localization to fine delineation. Extensive experiments on ten public medical segmentation datasets across four distinct tasks demonstrate that HFIBNet consistently outperforms existing state-of-the-art methods. The code is available available at https://github.com/ukeLin/HFIBNet.

Transformer-based models for time series forecasting have advanced considerably, yet many approaches treat attention sparsity and memory mechanisms as separate strategies. In this extended work, we revisit these paradigms and present LTSMiTransformer, a unified architecture that integrates Learnable Temporal Sparse Attention (LTSA), a Memory-Augmented Module (MAM) and a unified embedding strategy that enhances feature representation across heterogeneous datasets to improve efficiency, scalability, and generalization in long-horizon multivariate time series forecasting. LTSA employs a trainable threshold to dynamically filter irrelevant time steps, reducing attention complexity from O (L²) to O (L log L), while MAM encodes long-term patterns into a compact memory bank with gated updates, enabling persistent context retention without uncontrolled memory growth. Unlike prior models, these components are jointly optimized to balance sparsity and memory in a coherent training framework. This extended work provides new theoretical analysis of LTSA’s sub-quadratic efficiency and MAM’s convergence behaviour under sparse gating. Comprehensive robustness tests under noise, missing values, and temporal imbalance, along with evaluations on eight diverse datasets from finance, energy, weather, and traffic, demonstrate LTSMiTransformer’s accuracy, generalizability, and compactness. The theoretical proofs, ablation studies, and robustness evaluations included show that the LTSMiTransformer achieves a strong balance between scalability, interpretability, and generalization across diverse datasets. This paper significantly expands our earlier CNIOT 2025 conference papers by adding new theoretical results, deeper joint-training analysis, and wider empirical validation. All source code and datasets are available on GitHub to support reproducibility.