The great success achieved by deep neural networks attracts increasing attention from the manufacturing and healthcare communities. However, the limited availability of data and high costs of data collection are the major challenges for the applications in those fields. We propose in this work AISEL, an active image synthesis method for efficient labeling, to improve the performance of the small-data learning tasks. Specifically, a complementary AISEL dataset is generated, with labels actively acquired via a physics-based method to incorporate underlining physical knowledge at hand. An important component of our AISEL method is the bidirectional generative invertible network (GIN), which can extract interpretable features from the training images and generate physically meaningful virtual images. Our AISEL method then efficiently samples virtual images not only further exploits the uncertain regions but also explores the entire image space. We then discuss the interpretability of GIN both theoretically and experimentally, demonstrating clear visual improvements over the benchmarks. Finally, we demonstrate the effectiveness of our AISEL framework on aortic stenosis application, in which our method lowers the labeling cost by 90 percent while achieving a 15 percent improvement in prediction accuracy.

中文翻译：

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用于高效标记的主动图像合成

深度神经网络取得的巨大成功吸引了制造业和医疗保健界越来越多的关注。然而，数据的有限可用性和数据收集的高成本是这些领域应用的主要挑战。我们在这项工作中提出了 AISEL，一种用于有效标记的主动图像合成方法，以提高小数据学习任务的性能。具体来说，生成了一个互补的 AISEL 数据集，通过基于物理的方法主动获取标签，以结合手头的重点物理知识。我们 AISEL 方法的一个重要组成部分是双向生成可逆网络 (GIN)，它可以从训练图像中提取可解释的特征并生成具有物理意义的虚拟图像。然后我们的 AISEL 方法有效地对虚拟图像进行采样，不仅进一步利用了不确定区域，而且还探索了整个图像空间。然后，我们从理论上和实验上讨论了 GIN 的可解释性，展示了对基准的明显视觉改进。最后，我们证明了我们的 AISEL 框架在主动脉瓣狭窄应用中的有效性，其中我们的方法将标记成本降低了 90%，同时预测准确性提高了 15%。