arXiv 2507.043832025-07-06eess.IVcs.CV

ViTaL：面向多病理类型卵巢肿瘤识别的多模态数据集与基准

ViTaL: A Multimodality Dataset and Benchmark for Multi-pathological Ovarian Tumor Recognition

Department of Electronics and Information Engineering, Beihang University（电子信息工程系，北京航空航天大学）；Department of Gynecology and Obstetrics, Beijing Shijitan Hospital, Capital Medical University（妇产科，北京人民医院，首都医科大学）；Department of Computer Science, University of Liverpool（计算机科学系，利物浦大学）

You Zhou, Lijiang Chen, Guangxia Cui, Wenpei Bai, Yu Guo, Shuchang Lyu, Guangliang Cheng, Qi Zhao

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AI总结：

本文提出ViTaL多模态卵巢肿瘤数据集（496例患者、六类病理）及基于THOAM的ViTaL-Net基准模型，实现多病理分类并取得85%整体准确率。

摘要：

卵巢肿瘤作为一种常见的妇科疾病，若未能早期发现，可能迅速恶化为严重的健康危机，从而对女性健康构成重大威胁。深度神经网络具有识别卵巢肿瘤的潜力，从而降低死亡率，但有限的公开数据集阻碍了其发展。为填补这一空白，我们引入了一个重要的卵巢肿瘤病理识别数据集，称为ViTaL，该数据集包含496名患者、涵盖六种病理类别的视觉（Visual）、表格（Tabular）和语言（Linguistic）模态数据。ViTaL数据集包含三个子集，分别对应不同的患者数据模态：来自2216张二维超声图像的视觉数据、来自496名患者医学检查的表格数据，以及来自496名患者超声报告的语言数据。在临床实践中，仅区分良性与恶性卵巢肿瘤是不够的。为实现卵巢肿瘤的多病理类型分类，我们提出了一种基于三元组层次偏移注意力机制（THOAM）的ViTaL-Net，以最小化多模态数据特征融合过程中的损失。该机制能够有效增强不同模态信息之间的相关性和互补性。ViTaL-Net可作为卵巢肿瘤多病理类型、多模态分类任务的基准。在我们的综合实验中，所提出的方法表现出令人满意的性能，在两种最常见的卵巢肿瘤病理类型上取得了超过90%的准确率，整体性能达到85%。我们的数据集和代码可在https://github.com/GGbond-study/vitalnet获取。

英文摘要：

Ovarian tumor, as a common gynecological disease, can rapidly deteriorate into serious health crises when undetected early, thus posing significant threats to the health of women. Deep neural networks have the potential to identify ovarian tumors, thereby reducing mortality rates, but limited public datasets hinder its progress. To address this gap, we introduce a vital ovarian tumor pathological recognition dataset called \textbf{ViTaL} that contains \textbf{V}isual, \textbf{T}abular and \textbf{L}inguistic modality data of 496 patients across six pathological categories. The ViTaL dataset comprises three subsets corresponding to different patient data modalities: visual data from 2216 two-dimensional ultrasound images, tabular data from medical examinations of 496 patients, and linguistic data from ultrasound reports of 496 patients. It is insufficient to merely distinguish between benign and malignant ovarian tumors in clinical practice. To enable multi-pathology classification of ovarian tumor, we propose a ViTaL-Net based on the Triplet Hierarchical Offset Attention Mechanism (THOAM) to minimize the loss incurred during feature fusion of multi-modal data. This mechanism could effectively enhance the relevance and complementarity between information from different modalities. ViTaL-Net serves as a benchmark for the task of multi-pathology, multi-modality classification of ovarian tumors. In our comprehensive experiments, the proposed method exhibited satisfactory performance, achieving accuracies exceeding 90\% on the two most common pathological types of ovarian tumor and an overall performance of 85\%. Our dataset and code are available at https://github.com/GGbond-study/vitalnet.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。