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Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features

Publication ,  Conference
Saha, A; Tushar, FI; Faryna, K; D'Anniballe, VM; Hou, R; Mazurowski, MA; Rubin, GD; Lo, JY
Published in: Progress in Biomedical Optics and Imaging - Proceedings of SPIE
January 1, 2020

Weakly supervised disease classification of CT imaging suffers from poor localization owing to case-level annotations, where even a positive scan can hold hundreds to thousands of negative slices along multiple planes. Furthermore, although deep learning segmentation and classification models extract distinctly unique combinations of anatomical features from the same target class(es), they are typically seen as two independent processes in a computer-aided diagnosis (CAD) pipeline, with little to no feature reuse. In this research, we propose a medical classifier that leverages the semantic structural concepts learned via multi-resolution segmentation feature maps, to guide weakly supervised 3D classification of chest CT volumes. Additionally, a comparative analysis is drawn across two different types of feature aggregation to explore the vast possibilities surrounding feature fusion. Using a dataset of 1593 scans labeled on a case-level basis via rule-based model, we train a dual-stage convolutional neural network (CNN) to perform organ segmentation and binary classification of four representative diseases (emphysema, pneumonia/atelectasis, mass and nodules) in lungs. The baseline model, with separate stages for segmentation and classification, results in AUC of 0.791. Using identical hyperparameters, the connected architecture using static and dynamic feature aggregation improves performance to AUC of 0.832 and 0.851, respectively. This study advances the field in two key ways. First, case-level report data is used to weakly supervise a 3D CT classifier of multiple, simultaneous diseases for an organ. Second, segmentation and classification models are connected with two different feature aggregation strategies to enhance the classification performance.

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Published In

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

DOI

ISSN

1605-7422

ISBN

9781510633957

Publication Date

January 1, 2020

Volume

11314
 

Citation

APA
Chicago
ICMJE
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Saha, A., Tushar, F. I., Faryna, K., D’Anniballe, V. M., Hou, R., Mazurowski, M. A., … Lo, J. Y. (2020). Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (Vol. 11314). https://doi.org/10.1117/12.2550857
Saha, A., F. I. Tushar, K. Faryna, V. M. D’Anniballe, R. Hou, M. A. Mazurowski, G. D. Rubin, and J. Y. Lo. “Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features.” In Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Vol. 11314, 2020. https://doi.org/10.1117/12.2550857.
Saha A, Tushar FI, Faryna K, D’Anniballe VM, Hou R, Mazurowski MA, et al. Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2020.
Saha, A., et al. “Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features.” Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 11314, 2020. Scopus, doi:10.1117/12.2550857.
Saha A, Tushar FI, Faryna K, D’Anniballe VM, Hou R, Mazurowski MA, Rubin GD, Lo JY. Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2020.

Published In

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

DOI

ISSN

1605-7422

ISBN

9781510633957

Publication Date

January 1, 2020

Volume

11314