Identification of benign and malignant breast nodules on ultrasound: comparison of multiple deep learning models and model interpretation - Wen, Xi; Tu, Hao; Zhao, Bingyang; Zhou, Wenbo; Yang, Zhuo; Li, Lijuan (Frontiers in Oncology, 2025)
Background and PurposeDeep learning (DL) algorithms generally require full supervision of annotating the region of interest (ROI), a process that is both labor-intensive and susceptible to bias. We aimed to develop a weakly supervised algorithm to differentiate between benign and malignant breast tumors in ultrasound images without image annotation.MethodsWe developed and validated the models using two publicly available datasets: breast ultrasound image (BUSI) and GDPH&SYSUCC breast ultrasound datasets. After removing the poor quality images, a total of 3049 images were included, divided into two classes: benign (N = 1320 images) and malignant (N = 1729 images). Weakly-supervised DL algorithms were implemented with four networks (DenseNet121, ResNet50, EffientNetb0, and Vision Transformer) and trained using 2136 unannotated breast ultrasound images. 609 and 304 images were used for validation and test sets, respectively. Diagnostic performances were calculated as the area under the receiver operating characteristic curve (AUC). Using the class activation map to interpret the prediction results of weakly supervised DL algorithms.ResultsThe DenseNet121 model, utilizing complete image inputs without ROI annotations, demonstrated superior diagnostic performance in distinguishing between benign and malignant breast nodules when compared to ResNet50, EfficientNetb0, and Vision Transformer models. DenseNet121 achieved the highest AUC, with values of 0.94 on the validation set and 0.93 on the test set, significantly surpassing the performance of the other models across both datasets (all P < 0.05).ConclusionThe weakly supervised DenseNet121 model developed in this study demonstrated feasibility for ultrasound diagnosis of breast tumor and showed good capabilities in differential diagnosis. This model may help radiologists, especially novice doctors, to improve the accuracy of breast tumor diagnosis using ultrasound.