LightVM-SparseUNet: a lightweight medical image segmentation framework via Vision Mamba and sparse attention

TvaraNet: A Lightweight Mamba Neural Network for Real-Time Medical Image Segmentation

CENet: A lightweight context-enhanced network for efficient and accurate medical image classification

Accurate diagnosis from medical images is critical yet challenging in resource-constrained clinical settings, where models must balance high accuracy with computational efficiency for practical deployment. While deep learning has revolutionized medical image analysis, a significant gap persists: heavyweight models (e.g., Transformers, dense CNNs) achieve strong performance but are infeasible for edge devices, whereas existing lightweight architectures often lack the sophisticated context modeling needed for robust detection of subtle and heterogeneous pathologies. To address this challenges, we introduce the Context-Enhanced Network (CENet), an efficient convolutional architecture designed for medical image classification. CENet incorporates three key modules: (1) a Two-Level Feature Fusion Module (TLFM) that performs selective, gated fusion of only the most semantically complementary feature scales to prevent noise propagation; (2) a novel Triplet Excitation Attention (TEA), a parameter-efficient channel attention module for robust feature re-calibration; and (3) Lightweight Context-Enhanced Module (LightCEM) that enables iterative, self-guided feature refinement using global context. Extensive experiments across three medical imaging benchmarks, two brain tumor classification datasets (SARTAJ, Br35H) and dental radiography analysis demonstrate that CENet variants achieve state-of-the-art efficiency-accuracy trade-off. Notably, the CENet-small variant achieves weighted F1-score of 76.08% on the multi-class SARTAJ dataset while utilizing only 2.08 M parameters and 0.232 GFLOPs, outperforming larger architectures. Furthermore, Qualitative analyses using Grad-CAM further show that CENet consistently localizes clinically meaningful regions, enhancing interpretability. Meanwhile, CENet demonstrates real-time embedded performance on the NVIDIA Jetson Orin NX, achieving up to 75.17 FPS for CENet-small (and 62.21 FPS for CENet-large), which supports practical deployment under the strict latency and power constraints. While CENet achieves strong weighted performance across all benchmarks, classes with diffuse morphology (e.g., glioma) or subtle presentation (e.g., cavity) remain challenging and require further investigation. The code for this work will be made available at https://github.com/aminabenabid/CENet

LightFormer-3D: a lightweight hierarchical CNN-transformer hybrid for 3D medical image segmentation

Brain tumor segmentation from 3D MRI is critical for treatment planning, yet deploying accurate Transformer-based models in clinical settings remains challenging due to prohibitive computational costs. State-of-the-art methods often exceed 90M parameters and require seconds per inference, precluding real-time use on resource-constrained hardware. We propose LightFormer-3D, a lightweight hierarchical CNN-Transformer hybrid that applies a co-design principle to jointly optimize patch embedding, self-attention, and multi-scale feature fusion, ensuring that efficiency gains compound across all stages. Specifically, a Depthwise Separable Convolution Patch Embedding (DSC-PE) reduces embedding parameters by 70%; a Multi-head Self-Attention with Spatial Sequence Reduction (MSA-SR) compresses self-attention complexity from O(N 2 ) to O(N 2 /r 3 ); and a Lightweight Scalable Feature Fusion (LSFF) module adaptively weights multi-scale features using only five parameters. Evaluated on the BraTS 2017 and BraTS 2021 benchmarks, LightFormer-3D achieves a mean Dice score of 87.0% on BraTS 2021 with 1.78M parameters and 9.3 GFLOPs, surpassing UNETR (80.2% Dice, 92.5M parameters) by 6.8 pp and the best competing lightweight method, U-Net_ASPP_EVO (86.0%), by 1.0 pp, while delivering 10× faster inference than UNETR (0.51s vs. 5.41s per case on an NVIDIA RTX 4060). Five-fold cross-validation (86.7% ± 0.2%) confirms stable generalization. Ablation studies validate each module's contribution, and zero-shot external validation on the independent MU-Glioma-Post dataset (203 post-treatment cases) confirms robust whole-tumor localization (WT Dice 85.0%) under severe domain shift. LightFormer-3D establishes that co-designed lightweight modules can simultaneously improve accuracy and efficiency, achieving a new Pareto-optimal point for brain tumor segmentation.

High-precision image segmentation based on a hybrid CNN–Transformer architecture

The results indicate that the collaboration between global semantics and local details within a unified weighting domain can effectively improve the separability and deploy ability of high-resolution segmentation.

WVM-UNet: A Wavelet–Vision Mamba Framework for Enhanced Medical Image Segmentation

Accurate segmentation of skin lesions and gastrointestinal polyps is essential for early diagnosis and treatment planning. Currently, Convolutional Neural Networks (CNNs) are limited by local receptive fields, missing small lesions. While Transformers model global context, their quadratic computational complexity incurs high costs. To address these limitations, we propose the Wavelet–Vision Mamba UNet (WVM-UNet), integrating State Space Models (SSMs) for linear-complexity long-range dependencies and wavelet transforms for fine-grained feature extraction. The network employs a Wavelet-based Residual State Space (WRSS) block, combining the multi-scale decomposition of discrete wavelet transforms with Vision Mamba to efficiently capture global features. A Fused Channel–Spatial Attention (FCSA) mechanism is incorporated to adaptively recalibrate feature representations. Additionally, we construct an Encoder–Decoder Semantic Connection (EDSC) to replace traditional skip connections, effectively bridging the semantic gap between cross-level features. Experimental results on multiple public datasets demonstrate the competitive performance of our method. Specifically, on the ISIC 2017 dataset, WVM-UNet achieves an mIoU of 82.94% and a DSC of 90.67%, outperforming the Mamba-based VM-UNet by 2.71% in mIoU. These results indicate our architecture effectively captures discriminative features for precise medical image segmentation.

Leap-mamba: locality-enhanced feature calibration with pixel-region dual-stream vision mamba UNet for medical image segmentation

A Pixel–Region VSSblock that comprises a local-Mamba branch and a global-Mamba branch to simultaneously capture fine-grained details and high-level semantic context and a Hierarchical Feature Calibration Module (HFCM) that leverages multi-scale context awareness and a structural saliency filtering mechanism to dynamically calibrate the skip connections, effectively suppressing feature redundancy while enhancing semantic interaction quality.