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Low-cost Thyroid Nodule Diagnosis Using a Supervised Autoencoder Neural network

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posted on 2023-02-01, 04:48 authored by Michel BarlaudMichel Barlaud

Background: Thyroid Nodules are more and more widespread, with the frequency of thyroid cancers increasing accordingly. In routine practice, the practician's goal is to determine if a nodule is malignant or suspicious, and thus requires surgery. However, nearly one-third of thyroid nodules fall into the 'indeterminate' classification segment, often leading to the use of surgery as a diagnostic tool, when it could have been avoided with a better preoperative diagnosis. Molecular tests exist to address this issue, but they are markedly onerous.

Results: In this paper, we present an accurate, cost-less method to predict the malignancy of thyroid nodules using a categorical thyroid nodules dataset. The prediction is achieved through the use of a new supervised autoencoder neural network. Results show that the Supervised Autoencoder Neural Network outperforms classical classification methods on all evaluation metrics. A proof-of-concept Graphical User Interface was developed to demonstrate the medical usefulness of the latent space of the trained network.

Conclusions: Aside from the prediction itself, the Supervised Autoencoder Neural Network provides three medically interesting characteristics: a prediction confidence score, consistent feature ranking, and an interpretable latent space. All of those characteristics are valuable from the practician's point of view, as they allow for an efficient, cost-less and well-informed prognosis.




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Submitting Author's Institution

University Cote d'Azur France

Submitting Author's Country

  • France

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