用风格迁移提升乳腺超声图像增广效率，兼具可解释性与高性能。
A Novel Breast Ultrasound Image Augmentation Method Using Advanced Neural Style Transfer: An Efficient and Explainable Approach
- 基于高级神经风格迁移与GPU并行，实现高效图像增广。
- 在800张乳腺超声图上达到92.47%的增广准确率。
- 适合需要可解释性与算力优化的医学影像增广场景。
乳腺恶性肿瘤（BM）的临床诊断是当前医学领域的重要挑战。深度学习（DL）模型在早期诊断中表现优异，但受限于乳腺超声（BUS）图像数据量少，常出现过拟合问题。大规模BUS数据集因隐私和法律限制难以获取。因此，图像增广成为提升DL模型性能的必要步骤。然而，现有基于深度学习的增广方法存在黑箱操作、缺乏可解释性，且前后处理需大量计算资源与时间。为此，本研究提出一种结合先进神经风格迁移（NST）与可解释人工智能（XAI）的新型高效增广方法，利用DGX集群上的Horovod框架在8块GPU上分布式训练，实现5.09倍加速，同时保持模型精度。该方法在包含348例良性与452例恶性病例的800张BUS图像上验证，经多种定量分析显示，增广准确率达92.47%。
原文摘要 · Abstract (English)
Clinical diagnosis of breast malignancy (BM) is a challenging problem in the recent era. In particular, Deep learning (DL) models have continued to offer important solutions for early BM diagnosis but their performance experiences overfitting due to the limited volume of breast ultrasound (BUS) image data. Further, large BUS datasets are difficult to manage due to privacy and legal concerns. Hence, image augmentation is a necessary and challenging step to improve the performance of the DL models. However, the current DL-based augmentation models are inadequate and operate as a black box resulting lack of information and justifications about their suitability and efficacy. Additionally, pre and post-augmentation need high-performance computational resources and time to produce the augmented image and evaluate the model performance. Thus, this study aims to develop a novel efficient augmentation approach for BUS images with advanced neural style transfer (NST) and Explainable AI (XAI) harnessing GPU-based parallel infrastructure. We scale and distribute the training of the augmentation model across 8 GPUs using the Horovod framework on a DGX cluster, achieving a 5.09 speedup while maintaining the model's accuracy. The proposed model is evaluated on 800 (348 benign and 452 malignant) BUS images and its performance is analyzed with other progressive techniques, using different quantitative analyses. The result indicates that the proposed approach can successfully augment the BUS images with 92.47% accuracy.
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