59
IRUS Total
Downloads
  Altmetric

Weakly supervised representation learning for endomicroscopy image analysis

File Description SizeFormat 
paper888.pdfAccepted version948.72 kBAdobe PDFView/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