Explainable, Multi-modal Wound Infection Classification from Images Augmented with Generated Captions

TL;DR

This paper introduces SCARWID, a deep learning framework that combines synthetic captions and image data to improve wound infection detection, enhancing interpretability and accuracy in diabetic foot ulcer diagnosis.

Contribution

The study presents a novel multi-modal approach using synthetic captions and image-text fusion to improve infection classification accuracy and interpretability in wound analysis.

Findings

Achieved 0.85 sensitivity, 0.78 specificity, and 0.81 accuracy in infection detection.

Outperformed state-of-the-art models in wound infection classification.

Enhanced interpretability by displaying generated captions alongside images.

Abstract

Infections in Diabetic Foot Ulcers (DFUs) can cause severe complications, including tissue death and limb amputation, highlighting the need for accurate, timely diagnosis. Previous machine learning methods have focused on identifying infections by analyzing wound images alone, without utilizing additional metadata such as medical notes. In this study, we aim to improve infection detection by introducing Synthetic Caption Augmented Retrieval for Wound Infection Detection (SCARWID), a novel deep learning framework that leverages synthetic textual descriptions to augment DFU images. SCARWID consists of two components: (1) Wound-BLIP, a Vision-Language Model (VLM) fine-tuned on GPT-4o-generated descriptions to synthesize consistent captions from images; and (2) an Image-Text Fusion module that uses cross-attention to extract cross-modal embeddings from an image and its corresponding Wound-BLIP caption. Infection status is determined by retrieving the top-k similar items from a labeled support set. To enhance the diversity of training data, we utilized a latent diffusion model to generate additional wound images. As a result, SCARWID outperformed state-of-the-art models, achieving average sensitivity, specificity, and accuracy of 0.85, 0.78, and 0.81, respectively, for wound infection classification. Displaying the generated captions alongside the wound images and infection detection results enhances interpretability and trust, enabling nurses to align SCARWID outputs with their medical knowledge. This is particularly valuable when wound notes are unavailable or when assisting novice nurses who may find it difficult to identify visual attributes of wound infection.