用病灶引导的局部区域分析，提升卵巢超声分类准确率并减少标注工作量
Less Contouring, More Accuracy: Lesion-Guided ROI Deep Learning for Ovarian Ultrasound Classification
- 以病灶为中心划定兴趣区域，避免全图像素标注
- 在两个数据集上达到93.1%~97.6%准确率，AUC均达0.99
- 适合临床医生快速部署，兼顾精度与标注效率
经阴道超声对卵巢病变分类仍具挑战性，主要因影像特征重叠且依赖专家判断。本研究探讨病灶引导的感兴趣区域（ROI）深度学习能否在保持较高诊断性能的同时降低像素级病灶分割的标注负担。评估了两个公开卵巢超声数据集：用于八分类的多模态卵巢肿瘤超声（MMOTU）数据集和用于二分类的卵巢超声数据集（OUD）。在统一框架下比较四种策略：全局图像深度学习、病灶引导的ROI深度学习、病灶轮廓深度学习以及基于轮廓的放射组学结合机器学习分类器。测试了MaxViT-Tiny、Swin Transformer、EfficientNet-B7和ResNet18四种深度学习架构。放射组学模型采用支持向量机、K近邻和人工神经网络分类器，并对样本较少的OUD数据集使用ANOVA特征选择。病灶引导的ROI策略表现最佳，MaxViT-Tiny在MMOTU上达到93.10%准确率和0.99 AUC，在OUD上达到97.56%准确率和0.99 AUC。轮廓法虽精度相当，但标注成本显著更高。结果表明，病灶引导的ROI深度学习在诊断性能与标注效率间实现良好平衡，为可扩展的AI辅助卵巢超声分析提供实用方案。
原文摘要 · Abstract (English)
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
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