HYBRID DEEP REINFORCED REGRESSION FRAMEWORK FOR CARDIO-THORACIC RATIO MEASUREMENT

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HYBRID DEEP REINFORCED REGRESSION FRAMEWORK FOR CARDIO-THORACIC RATIO MEASUREMENT
Title:
HYBRID DEEP REINFORCED REGRESSION FRAMEWORK FOR CARDIO-THORACIC RATIO MEASUREMENT
Other Titles:
IEEE International Conference on Image Processing (ICIP)
Publication Date:
30 September 2020
Citation:
P. R. Singh, S. Gopalakrishnan, I. H. Mien and A. Ambikapathi, "Hybrid Deep Reinforced Regression Framework for Cardio-Thoracic Ratio Measurement," 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, 2020, pp. 433-437, doi: 10.1109/ICIP40778.2020.9191287.
Abstract:
Quantitative measurements obtained from medical images guide clinicians in several use cases but manually obtaining such measurements are both laborious and subject to interobserver variations. We develop a hybrid deep reinforced regression framework to robustly measure the Cardio-Thoracic ratio (CTR) from Chest X-ray (CXR) images, thereby directly identifying the presence of Cardiomegaly. The proposed hybrid framework initially employs a CNN based Regressor on pre-processed images to obtain approximate critical points. As the actual critical points are based on human expert’s experience and subject to labeling uncertainties, a deep reinforcement learning (deep RL) approach is specifically designed to fine-tune estimated regression points from the CNN Regressor. The final regressed points are then used to measure CTR. Wingspan and ChestX-ray8 datasets are used for validating the proposed framework. The proposed framework shows generalization ability on ChestX-ray8 and outperforms the state-of-the-art results on Wingspan.
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Funding Info:
This research is supported by core funding from the Institute of Infocomm Research
Description:
“© 2020 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.”
ISSN:
2381-8549
ISBN:
978-1-7281-6395-6
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