HealNet -- Self-Supervised Acute Wound Heal-Stage Classification

TL;DR

HealNet is a self-supervised learning framework that accurately classifies wound healing stages from visual data without requiring labeled datasets, aiding medical diagnosis and treatment planning.

Contribution

This work introduces a novel self-supervised approach for wound heal-stage classification that operates effectively on small, unlabeled datasets, reducing reliance on expert annotations.

Findings

Achieved 97.7% pre-text accuracy

Attained 90.62% heal-stage classification accuracy

Operates effectively with small, unlabeled datasets

Abstract

Identifying, tracking, and predicting wound heal-stage progression is a fundamental task towards proper diagnosis, effective treatment, facilitating healing, and reducing pain. Traditionally, a medical expert might observe a wound to determine the current healing state and recommend treatment. However, sourcing experts who can produce such a diagnosis solely from visual indicators can be difficult, time-consuming and expensive. In addition, lesions may take several weeks to undergo the healing process, demanding resources to monitor and diagnose continually. Automating this task can be challenging; datasets that follow wound progression from onset to maturation are small, rare, and often collected without computer vision in mind. To tackle these challenges, we introduce a self-supervised learning scheme composed of (a) learning embeddings of wound's temporal dynamics, (b) clustering for automatic stage discovery, and (c) fine-tuned classification. The proposed self-supervised and flexible learning framework is biologically inspired and trained on a small dataset with zero human labeling. The HealNet framework achieved high pre-text and downstream classification accuracy; when evaluated on held-out test data, HealNet achieved 97.7% pre-text accuracy and 90.62% heal-stage classification accuracy.