Deep learning has advanced medical image segmentation, yet existing methods struggle with complex anatomical structures. Mainstream models, such as CNN, Transformer, and hybrid architectures, face challenges including insufficient information representation and redundant complexity, which limit their clinical deployment. Developing efficient and lightweight networks is crucial for accurate lesion localization and optimized clinical workflows. We propose the VML-UNet, a lightweight segmentation network with core innovations including the CPMamba module and the multi-scale local supervision module (MLSM). The CPMamba module integrates the visual state space (VSS) block and a channel prior attention mechanism to enable efficient modeling of spatial relationships with linear computational complexity through dynamic channel-space weight allocation, while preserving channel feature integrity. The MLSM enhances local feature perception and reduces the inference burden. Comparative experiments were conducted on three public datasets, including ISIC2017, ISIC2018, and PH2, with ablation experiments performed on ISIC2017. VML-UNet achieves 0.53 M parameters, 2.18 MB memory usage, and 1.24 GFLOPs time complexity, with its performance on the datasets outperforming comparative networks, validating its effectiveness. This study provides valuable references for developing lightweight, high-performance skin lesion segmentation networks, advancing the field of skin lesion segmentation.

This study presents a systematic comparison of two dominant paradigms for improving rotation robustness in remote sensing semantic segmentation: rotation-invariant architectural modules (represented by Rotation-Invariant Channel Mapping, RICM) and random rotation data augmentation. Despite both approaches being widely adopted, a fair and side-by-side evaluation under unified experimental conditions remains lacking. Using U-Net as the baseline on the LoveDA dataset, we comprehensively evaluate six strategies: RICM alone, random rotation augmentation at probabilities $p=0.25$ and $p=0.5$, and their combinations. Performance is assessed under both discrete rotations (0°, 90°, 180°, 270°) and continuous angles (0°–180° with 15° intervals), supplemented by Rotation Consistency (RC) analysis, class-wise evaluation, and computational cost comparison. Cross-dataset validation on ISPRS Potsdam confirms generalizability. Our results demonstrate that: (1) random rotation augmentation consistently outperforms RICM, delivering substantial robustness gains with zero inference overhead; (2) augmentation probability controls a clear accuracy–robustness trade-off, with $p=0.25$ achieving the best balance (maintaining 0° mIoU at 74.8% while boosting 90° mIoU from 54.9% to 63.1%) and $p=0.5$ achieving near-invariance at the cost of reduced baseline accuracy; and (3) RICM offers only marginal benefits and becomes redundant when augmentation is applied. Analysis of the observed performance patterns suggests that early-stage RICM insertion may suppress useful orientation-specific features and that its rotation-ensemble averaging may be insufficient for dense prediction tasks. Overall, random rotation augmentation proves to be a simple, effective, and computationally efficient strategy for improving rotation robustness in remote sensing segmentation.