用AI自动识别卵巢超声图像中的多囊卵巢综合征，准确率达90.5%。
Leveraging AI for Automatic Classification of PCOS Using Ultrasound Imaging
- 基于InceptionV3的迁移学习，自动分类健康与异常超声帧
- 验证集上准确率90.52%，各项指标均超90%
- 结合LIME与显著性图提升模型可解释性，适合医疗AI研发者参考
AUTO-PCOS分类挑战旨在通过自动化分析超声图像提升人工智能在多囊卵巢综合征（PCOS）诊断中的能力。本报告介绍了利用InceptionV3架构进行迁移学习构建鲁棒AI流程的方法，实现了二分类的高精度。预处理优化了训练、验证和测试数据集，可解释性方法如LIME与显著性图揭示了模型决策逻辑。该方法在验证集上达到90.52%的准确率，精确率、召回率与F1分数均超过90%，证明了其有效性。研究强调了AI在医疗领域的变革潜力，特别是在应对如PCOS等诊断难题方面。关键发现、挑战及未来改进方向被讨论，指明了构建可靠、可解释且可扩展的AI医疗诊断工具的发展路径。
原文摘要 · Abstract (English)
The AUTO-PCOS Classification Challenge seeks to advance the diagnostic capabilities of artificial intelligence (AI) in identifying Polycystic Ovary Syndrome (PCOS) through automated classification of healthy and unhealthy ultrasound frames. This report outlines our methodology for building a robust AI pipeline utilizing transfer learning with the InceptionV3 architecture to achieve high accuracy in binary classification. Preprocessing steps ensured the dataset was optimized for training, validation, and testing, while interpretability methods like LIME and saliency maps provided valuable insights into the model's decision-making. Our approach achieved an accuracy of 90.52%, with precision, recall, and F1-score metrics exceeding 90% on validation data, demonstrating its efficacy. The project underscores the transformative potential of AI in healthcare, particularly in addressing diagnostic challenges like PCOS. Key findings, challenges, and recommendations for future enhancements are discussed, highlighting the pathway for creating reliable, interpretable, and scalable AI-driven medical diagnostic tools.
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