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CNN attention guidance for improved orthopedics radiographic fracture classification
Liao, Zhibin Liao, Kewen Shen, Haifeng van Boxel, Marouska F Prijs, Jasper Jaarsma, Ruurd L. Doornberg, Job N. van den Hengel, Anton Verjans, Johan W.
Liao, Zhibin
Liao, Kewen
Shen, Haifeng
van Boxel, Marouska F
Prijs, Jasper
Jaarsma, Ruurd L.
Doornberg, Job N.
van den Hengel, Anton
Verjans, Johan W.
Abstract
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.
Keywords
CNN, deep learning, network attention, orthopedics fracture classification, radiology, X-rays
Date
2022
Type
Journal article
Journal
IEEE Journal of Biomedical and Health Informatics
Book
Volume
26
Issue
7
Page Range
3139-3150
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
Collections
Relation URI
Source URL
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled