通过几何与光照变换提升医学图像分析性能
Enhancing Medical Image Analysis through Geometric and Photometric transformations
- 在皮肤癌数据集上应用多种变换增强数据
- 分类准确率从90.74%升至96.88%，损失降至0.1468
- 分割任务Dice系数由0提升至0.4163，适合医疗AI研究者
医学图像分析受限于标注数据不足，主要因患者隐私和专家稀缺。尽管某些AI模型需大量数据才能表现良好，本文探索通过数据增强提升模型性能并扩大数据集。首先，在包含良性和恶性类别的皮肤癌数据集上应用多种变换，训练卷积神经网络（CNN）后，测试准确率从90.74%提升至96.88%，测试损失从0.7921降至0.1468。其次，在视网膜血管数据集上采用Mixup技术混合两张随机图像及其对应掩码，训练U-Net模型后，Dice系数从0增至0.4163。结果表明，数据增强能有效提升分类与分割任务的性能。
原文摘要 · Abstract (English)
Medical image analysis suffers from a lack of labeled data due to several challenges including patient privacy and lack of experts. Although some AI models only perform well with large amounts of data, we will move to data augmentation where there is a solution to improve the performance of our models and increase the dataset size through traditional or advanced techniques. In this paper, we evaluate the effectiveness of data augmentation techniques on two different medical image datasets. In the first step, we applied some transformation techniques to the skin cancer dataset containing benign and malignant classes. Then, we trained the convolutional neural network (CNN) on the dataset before and after augmentation, which significantly improved test accuracy from 90.74% to 96.88% and decreased test loss from 0.7921 to 0.1468 after augmentation. In the second step, we used the Mixup technique by mixing two random images and their corresponding masks using the retina and blood vessels dataset, then we trained the U-net model and obtained the Dice coefficient which increased from 0 before augmentation to 0.4163 after augmentation. The result shows the effect of using data augmentation to increase the dataset size on the classification and segmentation performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。