Assessing Multimodal Chronic Wound Embeddings with Expert Triplet Agreement

TL;DR

This paper introduces TriDerm, a multimodal framework that learns interpretable wound representations from images, masks, and reports, achieving high agreement with experts in assessing chronic wound similarity.

Contribution

The work presents a novel multimodal approach combining visual and textual data with expert triplet comparisons to improve wound similarity assessment in rare diseases.

Findings

TriDerm achieves 73.5% agreement with experts, surpassing single-modality models.

Visual and textual modalities capture complementary wound features.

The framework enables interpretable wound representations from small cohorts.

Abstract

Recessive dystrophic epidermolysis bullosa (RDEB) is a rare genetic skin disorder for which clinicians greatly benefit from finding similar cases using images and clinical text. However, off-the-shelf foundation models do not reliably capture clinically meaningful features for this heterogeneous, long-tail disease, and structured measurement of agreement with experts is challenging. To address these gaps, we propose evaluating embedding spaces with expert ordinal comparisons (triplet judgments), which are fast to collect and encode implicit clinical similarity knowledge. We further introduce TriDerm, a multimodal framework that learns interpretable wound representations from small cohorts by integrating wound imagery, boundary masks, and expert reports. On the vision side, TriDerm adapts visual foundation models to RDEB using wound-level attention pooling and non-contrastive representation learning. For text, we prompt large language models with comparison queries and recover medically meaningful representations via soft ordinal embeddings (SOE). We show that visual and textual modalities capture complementary aspects of wound phenotype, and that fusing both modalities yields 73.5% agreement with experts, outperforming the best off-the-shelf single-modality foundation model by over 5.6 percentage points. We make the expert annotation tool, model code and representative dataset samples publicly available.