TransUNet-GradCAM: A Hybrid Transformer-U-Net with Self-Attention and Explainable Visualizations for Foot Ulcer Segmentation

TL;DR

This paper introduces TransUNet-GradCAM, a hybrid transformer-U-Net model for foot ulcer segmentation that combines global attention with local detail, achieving high accuracy and strong generalization across diverse clinical datasets.

Contribution

The study presents a novel hybrid transformer-U-Net architecture with explainability, demonstrating superior segmentation performance and zero-shot transferability in clinical foot ulcer datasets.

Findings

Achieved a Dice score of 0.8886 on internal validation.

Demonstrated robust zero-shot transfer with Dice scores of 0.6209 and 0.7850 on external datasets.

High correlation (r=0.9749) between predicted and actual wound areas.

Abstract

Automated segmentation of diabetic foot ulcers (DFUs) plays a critical role in clinical diagnosis, therapeutic planning, and longitudinal wound monitoring. However, this task remains challenging due to the heterogeneous appearance, irregular morphology, and complex backgrounds associated with ulcer regions in clinical photographs. Traditional convolutional neural networks (CNNs), such as U-Net, provide strong localization capabilities but struggle to model long-range spatial dependencies due to their inherently limited receptive fields. To address this, we employ the TransUNet architecture, a hybrid framework that integrates the global attention mechanism of Vision Transformers (ViTs) into the U-Net structure. This combination allows the model to extract global contextual features while maintaining fine-grained spatial resolution. We trained the model on the public Foot Ulcer Segmentation Challenge (FUSeg) dataset using a robust augmentation pipeline and a hybrid loss function to mitigate class imbalance. On the internal validation set, the model achieved a Dice Similarity Coefficient (F1-score) of 0.8886 using an optimized threshold of 0.4843. Crucially, to assess generalizability, we performed external validation on two independent datasets: the AZH Wound Care Center dataset (n=278) and the Medetec dataset (n=152). Without any retraining, the model achieved Dice scores of 0.6209 and 0.7850, respectively, demonstrating robust zero-shot transferability to unseen clinical domains. Furthermore, clinical utility analysis revealed a strong correlation (Pearson r = 0.9749) between predicted and ground-truth wound areas. These outcomes demonstrate that our approach effectively integrates global and local feature extraction, offering a reliable, effective, and explainable solution for automated foot ulcer assessment.