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On smart gaze based annotation of histopathology images for training of deep convolutional neural networks
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Mariam, Komal, Afzal, Osama Mohammed, Hussain, Wajahat, Javed, Muhammad Umar, Kiyani, Amber, Rajpoot, Nasir M. (Nasir Mahmood), Khurram, Syed Ali and Khan, Hassan Aqeel (2022) On smart gaze based annotation of histopathology images for training of deep convolutional neural networks. IEEE Journal of Biomedical and Health Informatics . doi:10.1109/JBHI.2022.3148944 ISSN 2168-2194.
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Official URL: https://doi.org/10.1109/JBHI.2022.3148944
Abstract
Unavailability of large training datasets is a bottleneck that needs to be overcome to realize the true potential of deep learning in histopathology applications. Although slide digitization via whole slide imaging scanners has increased the speed of data acquisition, labeling of virtual slides requires a substantial time investment from pathologists. Eye gaze annotations have the potential to speed up the slide labeling process. This work explores the viability and timing comparisons of eye gaze labeling compared to conventional manual labeling for training object detectors. Challenges associated with gaze based labeling and methods to refine the coarse data annotations for subsequent object detection are also discussed. Results demonstrate that gaze tracking based labeling can save valuable pathologist time and delivers good performance when employed for training a deep object detector. Using the task of localization of Keratin Pearls in cases of oral squamous cell carcinoma as a test case, we compare the performance gap between deep object detectors trained using hand-labelled and gaze-labelled data. On average, compared to 'Bounding-box' based hand-labeling, gaze-labeling required 57.6% less time per label and compared to 'Freehand' labeling, gaze-labeling required on average 85% less time per label.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > RB Pathology T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
Library of Congress Subject Headings (LCSH): | Image processing -- Digital techniques, Pathology -- Data processing, Deep learning (Machine learning), Eye tracking | ||||||||
Journal or Publication Title: | IEEE Journal of Biomedical and Health Informatics | ||||||||
Publisher: | IEEE | ||||||||
ISSN: | 2168-2194 | ||||||||
Official Date: | 2022 | ||||||||
Dates: |
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DOI: | 10.1109/JBHI.2022.3148944 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 22 April 2022 | ||||||||
Date of first compliant Open Access: | 22 April 2022 |
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