用深度学习自动生成子宫超声图像描述，提升诊断效率。
Uterine Ultrasound Image Captioning Using Deep Learning Techniques
- 结合卷积网络与双向门控循环单元生成图像描述。
- 在BLEU和ROUGE指标上优于基线模型。
- 适合临床辅助诊断与医学影像智能分析场景。
医学影像已深刻改变医疗诊断与治疗规划，从早期的X光发展到如今的MRI、CT及超声等复杂技术。本文研究深度学习在医学图像描述生成中的应用，聚焦于子宫超声图像。这类图像在妇产科中对不同年龄群体的疾病诊断与监测至关重要，但因其复杂性和多样性，解读常具挑战性。为此，本文提出一种融合卷积神经网络与双向门控循环单元的深度学习系统，联合处理图像与文本特征，生成子宫超声图像的描述性文字。实验结果表明，该模型在生成准确、信息丰富的描述方面表现优异，相较基线方法在BLEU和ROUGE评分上均有提升。本研究旨在提升子宫超声图像的可解释性，助力医生实现及时、精准的诊断，从而改善患者诊疗质量。
原文摘要 · Abstract (English)
Medical imaging has significantly revolutionized medical diagnostics and treatment planning, progressing from early X-ray usage to sophisticated methods like MRIs, CT scans, and ultrasounds. This paper investigates the use of deep learning for medical image captioning, with a particular focus on uterine ultrasound images. These images are vital in obstetrics and gynecology for diagnosing and monitoring various conditions across different age groups. However, their interpretation is often challenging due to their complexity and variability. To address this, a deep learning-based medical image captioning system was developed, integrating Convolutional Neural Networks with a Bidirectional Gated Recurrent Unit network. This hybrid model processes both image and text features to generate descriptive captions for uterine ultrasound images. Our experimental results demonstrate the effectiveness of this approach over baseline methods, with the proposed model achieving superior performance in generating accurate and informative captions, as indicated by higher BLEU and ROUGE scores. By enhancing the interpretation of uterine ultrasound images, our research aims to assist medical professionals in making timely and accurate diagnoses, ultimately contributing to improved patient care.
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