IEEE J Biomed Health Inform. 2022 Jul;26(7):3139-3150. doi: 10.1109/JBHI.2022.3152267. Epub 2022 Jul 1.
Convolutional neural networks (CNNs) have gained significant popularity in orthopedic imaging in recent years due to their ability to solve fracture classification problems. A common criticism of CNNs is their opaque learning and reasoning process, making it difficult to trust machine diagnosis and the subsequent adoption of such algorithms in clinical setting. This is especially true when the CNN is trained with limited amount of medical data, which is a common issue as curating sufficiently large amount of annotated medical imaging data is a long and costly process. While interest has been devoted to explaining CNN learnt knowledge by visualizing network attention, the utilization of the visualized attention to improve network learning has been rarely investigated. This paper explores the effectiveness of regularizing CNN network with human-provided attention guidance on where in the image the network should look for answering clues. On two orthopedics radiographic fracture classification datasets, through extensive experiments we demonstrate that explicit human-guided attention indeed can direct correct network attention and consequently significantly improve classification performance. The development code for the proposed attention guidance is publicly available on https://github.com/zhibinliao89/fracture_attention_guidance.
卷积神经网络（CNNs）近年来在骨科成像中得到了广泛的应用，因为它们能够解决骨折分类问题。人们普遍批评 CNN 的学习和推理过程不透明，这使得人们难以信任机器诊断，也难以在临床环境中采用此类算法。当 CNN 用有限数量的医疗数据进行训练时，情况尤其如此，因为编纂足够大量的带注释的医学成像数据是一个漫长而昂贵的过程。虽然人们已经致力于通过可视化网络注意力来解释 CNN 学习的知识，但很少有研究利用可视化注意力来改进网络学习。本文探讨了在图像的哪些区域，网络应该寻找线索来回答问题，用人为提供的注意力指导来正则化 CNN 网络的有效性。在两个骨科射线照相骨折分类数据集上，通过广泛的实验，我们证明了显式的人为引导注意力确实可以指导正确的网络注意力，从而显著提高分类性能。所提出的注意力指导的开发代码可在 https://github.com/zhibinliao89/fracture_attention_guidance 上获得。
