{"ID":2861081,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.03216","arxiv_id":"2510.03216","title":"Wave-GMS: Lightweight Multi-Scale Generative Model for Medical Image Segmentation","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2510.03216","url_pdf":"https://arxiv.org/pdf/2510.03216v1","authors":"[\"Talha Ahmed\",\"Nehal Ahmed Shaikh\",\"Hassan Mohy-ud-Din\"]","published":"2025-10-03T17:53:16Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.AI\",\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":608787,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2861081,"paper_url":"https://arxiv.org/abs/2510.03216","paper_title":"Wave-GMS: Lightweight Multi-Scale Generative Model for Medical Image Segmentation","repo_url":"https://github.com/ATPLab-LUMS/Wave-GMS","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}