mlsanityData to 7 Oct 2026

Wave-GMS: Lightweight Multi-Scale Generative Model for Medical Image Segmentation

arXiv 2510.03216 ↗First posted 3 Oct 2025eess.IV cs.AI cs.CVcited by 0

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Abstract (arXiv)

For equitable deployment of AI tools in hospitals and healthcare facilities, we need Deep Segmentation Networks that offer high performance and can be trained on cost-effective GPUs with limited memory and large batch sizes. In this work, we propose Wave-GMS, a lightweight and efficient multi-scale generative model for medical image segmentation. Wave-GMS has a substantially smaller number of trainable parameters, does not require loading memory-intensive pretrained vision foundation models, and supports training with large batch sizes on GPUs with limited memory. We conducted extensive experiments on four publicly available datasets (BUS, BUSI, Kvasir-Instrument, and HAM10000), demonstrating that Wave-GMS achieves state-of-the-art segmentation performance with superior cross-domain generalizability, while requiring only ~2.6M trainable parameters. Code is available at https://github.com/ATPLab-LUMS/Wave-GMS.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 37 references that are in the map. First posted on arXiv 3 Oct 2025.

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27 of 37 references.

References · 27

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  4. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  5. 2023Exploiting Diffusion Prior for Real-World Image Super-Resolutioncited by 486
  6. 2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentationcited by 412
  7. 2020Learning Enriched Features for Real Image Restoration and Enhancementcited by 311
  8. 2022Transformers in Medical Imaging: A Surveycited by 245
  9. 2021UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformercited by 197
  10. 2019MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentationcited by 195
  11. 2022Medical Image Segmentation Review: The success of U-Netcited by 190
  12. 2024Swin-UMamba: Mamba-based UNet with ImageNet-based pretrainingcited by 179
15 more
  1. 2023MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformercited by 124
  2. 2022UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentationcited by 112
  3. 2024Towards Extreme Image Compression with Latent Feature Guidance and Diffusion Priorcited by 56
  4. 2023EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentationcited by 53
  5. 2023Generative Semantic Segmentationcited by 32
  6. 2022DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentationcited by 27
  7. 2023On Efficient Training of Large-Scale Deep Learning Models: A Literature Reviewcited by 26
  8. 2023ACC-UNet: A Completely Convolutional UNet model for the 2020scited by 26
  9. 2024LiteVAE: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Modelscited by 22
  10. 2025Exploring Representation-Aligned Latent Space for Better Generationcited by 21
  11. 2024Stable Diffusion Segmentation for Biomedical Images with Single-step Reverse Processcited by 16
  12. 2024Generative Medical Segmentationcited by 6
  13. 2022Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directionscited by 5
  14. 2025WaveFormer: A 3D Transformer with Wavelet-Driven Feature Representation for Efficient Medical Image Segmentationcited by 2
  15. 2025MLRU++: Multiscale Lightweight Residual UNETR++ with Attention for Efficient 3D Medical Image Segmentationcited by 1

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