2022/02/22 by Zhibin Liao, Kewen Liao, Haifeng Shen +7 · 26 citations
Computer Science · Engineering · Medicine · #Artificial Intelligence in Healthcare and Education #Artificial intelligence #Artificial neural network #COVID-19 diagnosis using AI #Computer science #Contextual image classification #Convolutional neural network #Deep learning #Image (mathematics) #Machine learning #Medical Imaging and Analysis #Medical imaging #Medicine #Orthopedic surgery #Popularity #Process (computing) #cs.CV #cs.LG
paper · pdf · open access · doi:10.1109/jbhi.2022.3152267
published in IEEE Journal of Biomedical and Health Informatics 26(7), 3139-3150 (Institute of Electrical and Electronics Engineers) · 12 pages, Published in IEEE Journal of Biomedical and Health Informatics
openalex publication_date 2022/02/22 · arxiv created 2022/03/21 · arxiv updated 2022/03/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.