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.
翻译:确定、跟踪和预测伤口愈合阶段的进展是正确诊断、有效治疗、促进愈合和减少疼痛的根本任务。传统上,医学专家可以观察伤口以确定目前的愈合状态,并建议治疗。然而,仅仅从视觉指标中寻找能够进行这种诊断的专家可能很困难、费时和费钱。此外,损伤可能需要几周时间才能进行愈合过程,需要资源来持续监测和诊断。这项任务的自动化可能具有挑战性;从开始到成熟阶段的伤口进展之后的数据集很小,很少,而且往往收集而没有计算机的眼光。为了应对这些挑战,我们引入了一种自我监督的学习计划,其中包括:(a) 学习伤口的时间动态嵌入;(b) 自动阶段发现聚集,以及(c) 微调分类。拟议的自我监督和灵活的学习框架在生物学上受到启发,并经过培训,建立了带有零人类标签的小数据集。HealNet框架实现了高文本前和下游分类准确度;在评估搁置的测试数据时,HealNet实现了97.7%的精确度和90.0%的精确度。