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TauMed: test augmentation of deep learning in medical diagnosis

Published: 11 July 2021 Publication History

Abstract

Deep learning has made great progress in medical diagnosis. However, due to data standardization and privacy restriction, the acquisition and sharing of medical image data have been hindered, leading to the unacceptable accuracy of some intelligent medical diagnosis models. Another concern is data quality. If insufficient quantity and low-quality data are used for training and testing medical diagnosis models, it may cause serious medical accidents. We always use data augmentation to deal with it, and one of the most representative ways is through mutation relation. However, although common mutation methods can increase the amount of medical data, the quality of the image cannot be guaranteed due to the particularity of medical image. Therefore, combined with the characteristics of medical images, we propose TauMed, which implements augmentation techniques based on a series of mutation rules and domain semantics on medical datasets to generate sufficient and high-quality images. Moreover, we chose the ResNet-50 model to experiment with the augmented dataset and compared the results with two main popular mutation tools. The experimental result indicates that TauMed can improve the classification accuracy of the model effectively, and the quality of augmented images is higher than the other two tools. Its video is at https://www.youtube.com/watch?v=O8W8I7U_eqk and TauMed can be used at http://121.196.124.158:9500/.

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  • (2025)COVID-19 classification using CT images with Dense Cascade Neuro-Fuzzy NetworkAustralian Journal of Electrical and Electronics Engineering10.1080/1448837X.2025.2457281(1-14)Online publication date: 11-Feb-2025
  • (2024)A New Perspective of Deep Learning Testing Framework: Human-Computer Interaction Based Neural Network Testing2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10611437(16299-16305)Online publication date: 13-May-2024
  • (2024)DeepFeature: Guiding adversarial testing for deep neural network systems using robust featuresJournal of Systems and Software10.1016/j.jss.2024.112201(112201)Online publication date: Aug-2024
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cover image ACM Conferences
ISSTA 2021: Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis
July 2021
685 pages
ISBN:9781450384599
DOI:10.1145/3460319
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 11 July 2021

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Author Tags

  1. Data Augmentation
  2. Deep Learning
  3. Medical Image
  4. Mutation Rules

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Cited By

View all
  • (2025)COVID-19 classification using CT images with Dense Cascade Neuro-Fuzzy NetworkAustralian Journal of Electrical and Electronics Engineering10.1080/1448837X.2025.2457281(1-14)Online publication date: 11-Feb-2025
  • (2024)A New Perspective of Deep Learning Testing Framework: Human-Computer Interaction Based Neural Network Testing2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10611437(16299-16305)Online publication date: 13-May-2024
  • (2024)DeepFeature: Guiding adversarial testing for deep neural network systems using robust featuresJournal of Systems and Software10.1016/j.jss.2024.112201(112201)Online publication date: Aug-2024
  • (2023)Building an open-source system test generation tool: lessons learned and empirical analyses with EvoMasterSoftware Quality Journal10.1007/s11219-023-09620-w31:3(947-990)Online publication date: 6-Mar-2023
  • (2022)MetaA: Multi-Dimensional Evaluation of Testing Ability via Adversarial Examples in Deep Learning2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)10.1109/QRS57517.2022.00104(1004-1013)Online publication date: Dec-2022
  • (2021)TauAud: Test Augmentation of Image Recognition in Autonomous Driving2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C)10.1109/QRS-C55045.2021.00084(550-554)Online publication date: Dec-2021

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