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Machine learning with abstention for automated liver disease diagnosis
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Hamid, Kanza, Asi, Amina, Abbasi, Wajid Arshad, Sabih, Durre and Minhas, Fayyaz ul Amir Afsar (2018) Machine learning with abstention for automated liver disease diagnosis. In: 2017 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan, 18-20 Dec 2017. Published in: 2017 International Conference on Frontiers of Information Technology (FIT) ISBN 9781538635674. doi:10.1109/FIT.2017.00070
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WRAP-machine-learning-abstention-automated-liver-disease-Minhas-2017.pdf - Accepted Version - Requires a PDF viewer. Download (1168Kb) | Preview |
Official URL: https://ieeexplore.ieee.org/document/8261064
Abstract
This paper presents a novel approach for detection of liver abnormalities in an automated manner using ultrasound images. For this purpose, we have implemented a machine learning model that can, not only generate labels (normal and abnormal) for a given ultrasound image but, it can also detect when its prediction is likely to be incorrect. The proposed model abstains from generating the label of a test example if it is not confident about its prediction. Such behavior is commonly practiced by medical doctors who, when given insufficient information or a difficult case, can choose to carry out further clinical or diagnostic tests before generating a diagnosis. However, existing machine learning models are designed in a way to always generate a label for a given example even when the confidence of their prediction is low. We have proposed a novel stochastic gradient descent based solver for the learning with abstention paradigm and use it to make a practical, state of the art method for liver disease classification. The proposed method has been benchmarked on a data set of approximately 100 patients from MINAR, Multan, Pakistan and our results show that the performance of the proposed scheme is at par with medical experts.
| Item Type: | Conference Item (Paper) | |||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > RC Internal medicine |
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| Divisions: | Faculty of Science > Computer Science | |||||||||
| Library of Congress Subject Headings (LCSH): | Liver -- Diseases , Liver -- Diseases -- Data processing, Machine learning , Ultrasonic imaging, Liver -- Diseases -- Imaging | |||||||||
| Journal or Publication Title: | 2017 International Conference on Frontiers of Information Technology (FIT) | |||||||||
| Publisher: | IEEE | |||||||||
| ISBN: | 9781538635674 | |||||||||
| Official Date: | 18 January 2018 | |||||||||
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| DOI: | 10.1109/FIT.2017.00070 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Publisher Statement: | © 2017 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 coll | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
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| Conference Paper Type: | Paper | |||||||||
| Title of Event: | 2017 International Conference on Frontiers of Information Technology (FIT) | |||||||||
| Type of Event: | Conference | |||||||||
| Location of Event: | Islamabad, Pakistan | |||||||||
| Date(s) of Event: | 18-20 Dec 2017 | |||||||||
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