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

• URL: http://arxiv.org/abs/2206.10536v2
• Date: Thu, 23 Jun 2022 13:23:58 GMT
• Title: HealNet -- Self-Supervised Acute Wound Heal-Stage Classification
• Authors: H\'ector Carri\'on, Mohammad Jafari, Hsin-Ya Yang, Roslyn Rivkah Isseroff, Marco Rolandi, Marcella Gomez, Narges Norouzi
• Abstract summary: 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.
• Score: 2.3552433352583155
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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