Quantitative Analysis of the Impact of Region of Interest Information on Deep Learning Algorithms for Thyroid Ultrasound Imaging.

methodsWe compared a conventional convolutional neural network (CNN) trained without ROI information, interpreted through Grad-CAM for attention visualization, to Faster R-CNN and YOLOv2 models trained with radiologist-validated ROI masks. We also introduced an adapted mosaic-based composite input, derived from mosaic augmentation but implemented as fixed 1 2 and 2 2 layouts, to improve class balance and spatial diversity in training.

resultsModels trained with ROI guidance achieved higher performance in both localization and classification compared to those trained without ROI. The average classification accuracy increased from about 80 in the baseline CNN to around 85 in ROI-guided models that shows an improvement of approximately 5 percentage points. The mean intersection over union between detected and radiologist-defined ROIs increased from approximately 33 to over 70. The adapted mosaic input further stabilized performance across epochs and improved sensitivity while maintaining comparable specificity.

conclusionsIncorporating radiologist-defined ROI information and structured mosaic inputs significantly improves both diagnostic accuracy and localization precision. These results demonstrate that integrating ROI-guided learning with context-preserving composite inputs provides a reproducible framework for developing reliable AI systems in thyroid ultrasonography.