ROI+Context Graph Neural Networks for Thyroid Nodule Classification: Baselines, Cross-Validation Protocol, and Reproducibility

Accurate differentiation of malignant from benign thyroid nodules on ultrasound remains challenging and existing deep models typically operate on isolated ROI crops or whole images, ignoring structured peri-lesional context. We present a simple, reproducible graph neural network (GNN) baseline that represents each thyroid ultrasound image as a small graph with one lesion region-of-interest (ROI) node and multiple peri-lesional context nodes, aggregates node embeddings with attention pooling, and predicts malignancy. A ResNet-50 encoder (ImageNet initialization) provides visual features, and lightweight geometry (relative offsets, size ratio, IoU) augments nodes. Using the public TN5000 dataset, we obtain a single-split validation accuracy of 0.904, AUROC 0.942, and AUPRC 0.979 on the official train/validation split, and a more robust 5-fold × 3-seed cross-validation AUROC of 0.906 and AUPRC of 0.954. We also report calibration analysis with temperature scaling to encourage transparent, robust evaluation of thyroid ultrasound classifiers. To our knowledge this is the first ROI+context GNN study on TN5000, providing a transparent baseline and cross-validation protocol.