High-resolution (HR) medical images are preferred in clinical diagnoses and subsequent analysis. However, the acquisition of HR medical images is easily affected by hardware devices. As an effective and trusted alternative method, the super-resolution (SR) technology is introduced to improve the image resolution. Compared with traditional SR methods, the deep learning-based SR methods can obtain more clear and trusted HR images. In this paper, we propose a trusted deep convolutional neural network-based SR method named feedback adaptive weighted dense network (FAWDN) for HR medical image reconstruction. Specifically, the proposed FAWDN can transmit the information of the output image to the low-level features by a feedback connection. To explore advanced feature representation and reduce the feature redundancy in dense blocks, an adaptive weighted dense block (AWDB) is introduced to adaptively select the informative features. Experimental results demonstrate that our FAWDN outperforms the state-of-the-art image SR methods and can obtain more clear and trusted medical images than comparative methods.
A Trusted Medical Image Super-Resolution Method based on Feedback Adaptive Weighted Dense Network / L. Chen, X. Yang, G. Jeon, M. Anisetti, K. Liu. - In: ARTIFICIAL INTELLIGENCE IN MEDICINE. - ISSN 0933-3657. - 106(2020 Dec), pp. 101857.1-101857.12.
|Titolo:||A Trusted Medical Image Super-Resolution Method based on Feedback Adaptive Weighted Dense Network|
|Parole Chiave:||Medical image super-resolution; Trusted medical image reconstruction; Deep convolutional neural network; Feedback mechanism; Adaptive weighting|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Data di pubblicazione:||dic-2020|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.artmed.2020.101857|
|Appare nelle tipologie:||01 - Articolo su periodico|