Unsupervised Deep Transfer Feature Learning for Medical Image Classification

Ahn, Euijoon, Kumar, Ashnil, Feng, Dagan, Fulham, Michael, and Kim, Jinman (2019) Unsupervised Deep Transfer Feature Learning for Medical Image Classification. In: Proceedings of the IEEE 16th International Symposium on Biomedical Imaging. pp. 1915-1918. From: ISB I2019: IEEE 16th International Symposium on Biomedical Imaging, 8-11 April 2019, Venice, Italy.

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Abstract

The accuracy and robustness of image classification with supervised deep learning are dependent on the availability of large-scale, annotated training data. However, there is a paucity of annotated data available due to the complexity of manual annotation. To overcome this problem, a popular approach is to use transferable knowledge across different domains by: 1) using a generic feature extractor that has been pre-trained on large-scale general images (i.e., transfer-learned) but which not suited to capture characteristics from medical images; or 2) fine-tuning generic knowledge with a relatively smaller number of annotated images. Our aim is to reduce the reliance on annotated training data by using a new hierarchical unsupervised feature extractor with a convolutional auto-encoder placed atop of a pre-trained convolutional neural network. Our approach constrains the rich and generic image features from the pre-trained domain to a sophisticated representation of the local image characteristics from the unannotated medical image domain. Our approach has a higher classification accuracy than transfer-learned approaches and is competitive with state-of-the-art supervised fine-tuned methods.

Item ID: 72034
Item Type: Conference Item (Research - E1)
ISBN: 978-1-5386-3641-1
Copyright Information: © IEEE
Date Deposited: 19 Apr 2022 02:16
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing @ 60%
46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460308 Pattern recognition @ 40%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220404 Computer systems @ 60%
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 40%
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