Weakly supervised representation learning for endomicroscopy image analysis
File(s)
Accepted version
Author(s)
Gu, Y
Vyas, K
Yang, J
Yang, GZ
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.
Date Issued
2018-09-26
Date Acceptance
2018-09-16
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 11071 LNCS, pp.326-334
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
Source
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
Subjects
08 Information And Computing Sciences
Artificial Intelligence & Image Processing
Publication Status
Published
Start Date
2018-09-16
Finish Date
2018-09-20
Coverage Spatial
Granada, Spain
Date Publish Online
2018-09-26