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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