  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 GitHub.
