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Weakly supervised representation learning for endomicroscopy image analysis
File | Description | Size | Format | |
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paper888.pdf | Accepted version | 948.72 kB | Adobe PDF | View/Open |
Title: | Weakly supervised representation learning for endomicroscopy image analysis |
Authors: | Gu, Y Vyas, K Yang, J Yang, GZ |
Item Type: | Conference Paper |
Abstract: | This paper proposes a weakly-supervised representation learning framework for probe-based confocal laser endomicroscopy (pCLE). Unlike previous frame-based and mosaic-based methods, the proposed framework adopts deep convolutional neural networks and integrates frame-based feature learning, global diagnosis prediction and local tumor detection into a unified end-to-end model. The latent objects in pCLE mosaics are inferred via semantic label propagation and the deep convolutional neural networks are trained with a composite loss function. Experiments on 700 pCLE samples demonstrate that the proposed method trained with only global supervisions is able to achieve higher accuracy on global and local diagnosis prediction. |
Issue Date: | 26-Sep-2018 |
Date of Acceptance: | 16-Sep-2018 |
URI: | http://hdl.handle.net/10044/1/65139 |
DOI: | https://dx.doi.org/10.1007/978-3-030-00934-2_37 |
ISBN: | 9783030009335 |
ISSN: | 0302-9743 |
Publisher: | Springer |
Start Page: | 326 |
End Page: | 334 |
Journal / Book Title: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Volume: | 11071 LNCS |
Copyright Statement: | © 2018 Springer Nature Switzerland AG. The final publication is available at Springer via https://dx.doi.org/10.1007/978-3-030-00934-2_37 |
Conference Name: | Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 |
Keywords: | 08 Information And Computing Sciences Artificial Intelligence & Image Processing |
Publication Status: | Published |
Start Date: | 2018-09-16 |
Finish Date: | 2018-09-20 |
Conference Place: | Granada, Spain |
Online Publication Date: | 2018-09-26 |
Appears in Collections: | Computing Faculty of Engineering |