Transformer models show better generalization in diabetic foot ulcer segmentation

A new study benchmarks three deep learning models for diabetic foot ulcer segmentation: U-Net, DeepLabV3+, and SegFormer-B2. While all models performed well on their training datasets, their accuracy significantly degraded when tested on external datasets. The Transformer-based SegFormer-B2 demonstrated superior generalization capabilities compared to the convolutional neural network models, suggesting that architecture type is a key factor in cross-hospital performance. AI

IMPACT Transformer architectures may offer better cross-dataset generalization for medical image segmentation tasks.

RANK_REASON The cluster contains an academic paper detailing a benchmark study of AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• Abderrahmane Benfatah
• Al-Azhar University
• Deeplabv3 Plus
• DFUC2022
• Kazuhiro Fusegawa
• MeDetect: Domain Entity Annotation in Biomedical References Using Linked Open Data
• SegFormer-b2
• U-Net

• paper
• model release

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