arXiv：cs.AI· Vanessa Borst, Timo Dittus, Tassilo Dege, Astrid Schmieder, Samuel Kounev·· 13 小时前
WoundAmbit：打通SOTA语义分割与真实伤口护理
WoundAmbit: Bridging State-of-the-Art Semantic Segmentation and Real-World Wound Care
AI 导读
WoundAmbit 将 AI 伤口尺寸估计框架集成进定制远程医疗系统，并系统评测了通用视觉、医学影像及公开伤口挑战赛的先进分割模型。基于 Transformer 的 TransNeXt 泛化能力最强，VWFormer 与 ConvNeXtS 骨干在医生评估中掩码通过率最高；所有模型在 CPU 上均达每秒至少一张图像的推理速度，各模型尺寸检索精度相近且预测与专家标注高度一致。
正文
Abstract:Chronic wounds affect a large population, particularly the elderly and diabetic patients, who often exhibit limited mobility and co-existing health conditions. Automated wound monitoring via mobile image capture can reduce in-person physician visits by enabling remote tracking of wound size. Semantic segmentation is key to this process, yet wound segmentation remains underrepresented in medical imaging research. To address this, we benchmark state-of-the-art deep learning models from general-purpose vision, medical imaging, and top methods from public wound challenges. For a fair comparison, we standardize training, data augmentation, and evaluation, conducting cross-validation to minimize partitioning bias. We also assess real-world deployment aspects, including generalization to an out-of-distribution wound dataset, computational efficiency, and interpretability. Additionally, we propose a reference object-based approach to convert AI-generated masks into clinically relevant wound size estimates and evaluate this, along with mask quality, for the five best architectures based on physician assessments. Overall, the transformer-based TransNeXt showed the highest levels of generalizability. Despite variations in inference times, all models processed at least one image per second on the CPU, which is deemed adequate for the intended application. Interpretability analysis typically revealed prominent activations in wound regions, emphasizing focus on clinically relevant features. Expert evaluation showed high mask approval for all analyzed models, with VWFormer and ConvNeXtS backbone performing the best. Size retrieval accuracy was similar across models, and predictions closely matched expert annotations. Finally, we demonstrate how our AI-driven wound size estimation framework, WoundAmbit, is integrated into a custom telehealth system.
| Comments: | Main paper: 18 pages; supplementary material: 15 pages |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2504.06185 [cs.CV] |
| (or arXiv:2504.06185v3 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2504.06185 arXiv-issued DOI via DataCite |
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| Journal reference: | Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track (ECML PKDD 2025). Lecture Notes in Computer Science, vol. 16021, pp. 285-303. Springer, Cham (2026) |
| Related DOI: | https://doi.org/10.1007/978-3-032-06118-8_17
DOI(s) linking to related resources |
Submission history
From: Vanessa Borst [view email]
[v1]
Tue, 8 Apr 2025 16:25:59 UTC (23,298 KB)
[v2]
Wed, 25 Jun 2025 09:21:21 UTC (23,309 KB)
[v3]
Fri, 9 Oct 2026 16:07:10 UTC (23,571 KB)
来源：arXiv：cs.AI · arxiv.org