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 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 94.2% pre-text accuracy and 93.8% heal-stage classification accuracy.

本文介绍了一种基于自监督学习方案的伤口治愈进程自动化诊断方法，使用学习的嵌入来表示伤口的时间动态，并进行聚类以自动发现不同的治愈阶段。该方法在小型数据集上进行训练，并取得了较高的预文本和各治愈阶段分类准确率。

HealNet -- 自监督的急性伤口愈合阶段分类