标题： 一种基于先进神经风格迁移的新型乳腺超声图像增强方法：一种高效且可解释的方法 显示英文标题

标题： A Novel Breast Ultrasound Image Augmentation Method Using Advanced Neural Style Transfer: An Efficient and Explainable Approach

摘要： 乳腺恶性肿瘤（BM）的临床诊断是当今时代的一个难题。 特别是，深度学习（DL）模型一直在为早期BM诊断提供重要的解决方案，但由于乳腺超声（BUS）图像数据量有限，其性能会遇到过拟合问题。 此外，由于隐私和法律方面的顾虑，大型BUS数据集难以管理。 因此，图像增强是提高DL模型性能的一个必要且具有挑战性的步骤。 然而，目前基于DL的增强模型尚不完善，且作为黑盒操作，缺乏关于其适用性和有效性的信息和解释。 此外，预处理和后处理需要高性能的计算资源和时间来生成增强图像并评估模型性能。 因此，本研究旨在开发一种针对BUS图像的新型高效增强方法，结合先进的神经风格迁移（NST）和可解释性人工智能（XAI），利用基于GPU的并行基础设施。 我们使用Horovod框架在DGX集群中的8个GPU上扩展和分布训练增强模型，实现了5.09倍的速度提升，同时保持了模型的准确性。 所提出的模型在800张BUS图像（348张良性，452张恶性）上进行了评估，并通过不同的定量分析与其他先进方法进行了性能对比分析。 结果显示，所提出的方法能够成功地以92.47%的准确率增强BUS图像。 显示英文摘要

摘要： 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.