Abstract
This paper presents an approach for the automated analysis of 3D Computed Tomography (CT) images based on the utilization of descriptors extracted using 3D deep convolutional autoencoder (AEC [8]) networks. Both the common flow of AEC model application and a set of techniques for overcoming the lack of training samples are presented in this work. The described approach was used for accomplishing the two subtasks of the ImageCLEF 2019: Tuberculosis competition [2, 5] and allowed to achieve the 2nd best performance in the TB Severity Scoring subtask and the 6th best performance in the TB CT Report subtask.
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Participant name was changed during competition.
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Acknowledgements
This study was partly supported by the National Institute of Allergy and Infectious Diseases, National Institutes of Health, U.S. Department of Health and Human Services, USA through the CRDF project DAA3–18-64818-1 “Year 7: Belarus TB Database and TB Portals”.
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Kazlouski, S. (2020). Tuberculosis CT Image Analysis Using Image Features Extracted by 3D Autoencoder. In: Arampatzis, A., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2020. Lecture Notes in Computer Science(), vol 12260. Springer, Cham. https://doi.org/10.1007/978-3-030-58219-7_12
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DOI: https://doi.org/10.1007/978-3-030-58219-7_12
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