Beyond Labels: Zero-Shot Diabetic Foot Ulcer Wound Segmentation with Self-attention Diffusion Models and the Potential for Text-Guided Customization

Abstract: Diabetic foot ulcers (DFUs) pose a significant challenge in healthcare, requiring precise and efficient wound assessment to enhance patient outcomes. This study introduces the Attention Diffusion Zero-shot Unsupervised System (ADZUS), a novel text-guided diffusion model that performs wound segmentation without relying on labeled training data. Unlike conventional deep learning models, which require extensive annotation, ADZUS leverages zero-shot learning to dynamically adapt segmentation based on descriptive prompts, offering enhanced flexibility and adaptability in clinical applications. Experimental evaluations demonstrate that ADZUS surpasses traditional and state-of-the-art segmentation models, achieving an IoU of 86.68\% and the highest precision of 94.69\% on the chronic wound dataset, outperforming supervised approaches such as FUSegNet. Further validation on a custom-curated DFU dataset reinforces its robustness, with ADZUS achieving a median DSC of 75\%, significantly surpassing FUSegNet's 45\%. The model's text-guided segmentation capability enables real-time customization of segmentation outputs, allowing targeted analysis of wound characteristics based on clinical descriptions. Despite its competitive performance, the computational cost of diffusion-based inference and the need for potential fine-tuning remain areas for future improvement. ADZUS represents a transformative step in wound segmentation, providing a scalable, efficient, and adaptable AI-driven solution for medical imaging.

ADZUS: Zero-Shot Wound Segmentation via Self-Attention Diffusion Models

This paper introduces the Attention Diffusion Zero-shot Unsupervised System (ADZUS), a diffusion model designed to tackle diabetic foot ulcer (DFU) wound segmentation without relying on labeled training data. ADZUS employs self-attention mechanisms and harnesses zero-shot learning, facilitating dynamic adaptation to descriptive textual prompts. The model offers a promising alternative to traditional deep learning approaches, which often depend heavily on annotated data, showcasing a novel method for wound segmentation that improves flexibility and adaptability in clinical settings.

Key findings demonstrate that ADZUS outperforms existing benchmarks significantly. On a chronic wound dataset, ADZUS achieved an IoU of 86.68% and precision of 94.69%, surpassing the supervised FUSegNet's respective IoU of 86.40% and precision of 94.40%. On a custom-curated DFU dataset, ADZUS exhibited a median DSC of 75%, outperforming FUSegNet's 45%. This indicates ADZUS's robustness across varied clinical cases, highlighting its potential as a scalable and efficient solution for medical imaging without the need for annotated training data.

The implications of this research extend both practically and theoretically. Practically, ADZUS provides clinicians with an adaptable, text-guided imaging technology capable of real-time customization based on clinical descriptions. This capability allows for targeted analysis, minimizing the reliance on expert annotation and manual inspection, which varies by experience and setting. Theoretically, the paper suggests diffusion models can address segmentation challenges in medical imaging, where subtle differences are critical for diagnostic accuracy.

The use of ADZUS for zero-shot segmentation aligns with ADZUS's significant advances, rendering it efficient even with computational costs associated with diffusion-based inference. While the need for fine-tuning suggests further development potential, opportunities exist to leverage ADZUS's text-guided features for enhanced AI-driven diagnostics and broader applications across different medical imaging challenges.

The future progression of AI in medical imaging may benefit from exploring ADZUS in multimodal contexts, integrating electronic health records or histopathological data, and applying domain adaptation techniques. ADZUS's architecture, utilizing self-attention within stable diffusion models, represents a promising advance towards more autonomous, efficient, and reliable medical imaging solutions.

Overall, this paper underscores a transformative approach to wound segmentation, highlighting ADZUS's adaptability without dependency on labeled data. Its progressive results suggest potential for broader explorations in autonomous medical imaging tasks, advancing the intelligent interaction between AI systems and clinical applications.