A Deep Learning Framework for Thyroid Nodule Segmentation and Malignancy Classification from Ultrasound Images

TL;DR

This paper introduces an interpretable, fully automated deep learning pipeline for thyroid nodule segmentation and malignancy classification from ultrasound images, outperforming traditional feature-based methods.

Contribution

It presents the first end-to-end deep learning framework combining nodule segmentation and malignancy prediction with interpretability.

Findings

Achieved an F1-score of 0.852 for malignancy prediction.

Outperformed a baseline with hand-crafted features (F1-score 0.829).

Demonstrated that learned visual features are more predictive than shape features.

Abstract

Ultrasound-based risk stratification of thyroid nodules is a critical clinical task, but it suffers from high inter-observer variability. While many deep learning (DL) models function as "black boxes," we propose a fully automated, two-stage framework for interpretable malignancy prediction. Our method achieves interpretability by forcing the model to focus only on clinically relevant regions. First, a TransUNet model automatically segments the thyroid nodule. The resulting mask is then used to create a region of interest around the nodule, and this localised image is fed directly into a ResNet-18 classifier. We evaluated our framework using 5-fold cross-validation on a clinical dataset of 349 images, where it achieved a high F1-score of 0.852 for predicting malignancy. To validate its performance, we compared it against a strong baseline using a Random Forest classifier with hand-crafted morphological features, which achieved an F1-score of 0.829. The superior performance of our DL framework suggests that the implicit visual features learned from the localised nodule are more predictive than explicit shape features alone. This is the first fully automated end-to-end pipeline for both detecting thyroid nodules on ultrasound images and predicting their malignancy.