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Joseph Cox

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Proceedings Article | 16 March 2020 Paper

Hyperparameter selection for ResNet classification of malignancy from thyroid ultrasound images

Joseph Cox, Sydney Rubin, Joe Adams, Carina Pereira, Manjiri Dighe, Adam Alessio

Proceedings Volume 11314, 1131447 (2020) https://doi.org/10.1117/12.2550531

KEYWORDS: Data modeling, RGB color model, Ultrasonography, Performance modeling, Image classification, Elastography, Biopsy, Fine needle aspiration, Optimization (mathematics)

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Thyroid nodules are extremely common, with a prevalence of up to 68% in adults. Ultrasound imaging is usually performed to detect and evaluate thyroid nodules for malignancy. Many patients undergo follow-up biopsy in the form of fine-needle aspiration (FNA) to determine if a nodule is malignant or benign, although most nodules are benign. In order to reduce the number of unnecessary FNAs, radiologists will often use classification systems such as Thyroid Imaging, Reporting, and Data System (TI-RADS) to provide risk stratification and a recommendation regarding whether FNA is necessary. This scoring is both subjective and time-consuming, leading to discrepancies between radiologists and recommendations that can be inaccurate. We hypothesize that a machine learned classifier can be identified with accurate and generalizable performance, potentially offering more consistent results than manual evaluation. We created a network from two ResNet-50 branches accepting two inputs, shear-wave elastography and B-mode ultrasound images. We performed a grid search to determine the optimal hyperparameters for our model, resulting in a network that predicted malignancy of nodules with 88.7% accuracy and an AUC of 0.91. Along with identifying the training hyperparameters with optimal classification accuracy, the grid search also allowed us to select training parameters that led to more generalizable model performance on test data sets. These initial performance results suggest that our model offers a promising strategy for thyroid nodule classification and a strategy to help identify more generalizable models.

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