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This study explores lesion-guided region-of-interest (ROI) deep learning for ovarian ultrasound classification, aiming to enhance diagnostic accuracy while minimizing the annotation burden associated with pixel-level segmentation. By evaluating four strategies across two publicly available datasets, the research reveals that the lesion-guided ROI approach outperforms traditional methods, achieving up to 97.56% accuracy and an AUC of 0.99. The findings underscore the potential of this method to streamline the classification process without sacrificing performance, making it a viable option for scalable AI-assisted analysis in clinical settings.
Lesion-guided ROI deep learning achieves up to 97.56% accuracy in ovarian ultrasound classification while significantly reducing the annotation workload.
Ovarian lesion classification using transvaginal ultrasound remains challenging due to overlapping imaging characteristics and the dependence on expert interpretation. This study investigates whether lesion-guided region-of-interest (ROI) deep learning can achieve competitive diagnostic performance while reducing the annotation burden associated with pixel-level lesion segmentation. Two publicly available ovarian ultrasound datasets were evaluated: the Multi-Modality Ovarian Tumor Ultrasound (MMOTU) dataset for eight-class classification and the Ovarian Ultrasound Dataset (OUD) for binary classification. Four strategies were compared under a unified framework: global image-based deep learning, lesion-guided ROI-based deep learning, lesion contour-based deep learning, and contour-based radiomics with machine learning classifiers. Four deep learning architectures, MaxViT-Tiny, Swin Transformer, EfficientNet-B7, and ResNet18, were evaluated. Radiomics models were developed using support vector machine, k-nearest neighbors, and artificial neural network classifiers, with ANOVA-based feature selection applied for the lower-sample OUD dataset. The lesion-guided ROI strategy achieved the strongest overall performance, with MaxViT-Tiny obtaining 93.10% accuracy and an AUC of 0.99 on MMOTU and 97.56% accuracy and an AUC of 0.99 on OUD. The contour-based approach achieved comparable accuracy but required substantially higher annotation effort. These findings demonstrate that lesion-guided ROI deep learning provides an effective balance between diagnostic performance and annotation efficiency, offering a practical approach for scalable AI-assisted ovarian ultrasound analysis
