NL repository
menu
검색
Library
Browse
Communities & Collections
By Date
Authors
Titles
Subject
My Repository
My Account
Receive email updates
Edit Profile
DSpace at EWHA
인공지능대학
인공지능학과
Journal papers
View : 224 Download: 0
Efficient Deep-Detector Image Quality Assessment Based on Knowledge Distillation
Title
Efficient Deep-Detector Image Quality Assessment Based on Knowledge Distillation
Authors
Lee
;
Wonkyeong
;
Gold
;
Garry Evan
;
Choi
;
Jang-Hwan
Ewha Authors
최장환
SCOPUS Author ID
최장환
Issue Date
2024
Journal Title
IEEE Transactions on Instrumentation and Measurement
ISSN
0018-9456
Citation
IEEE Transactions on Instrumentation and Measurement vol. 73, pp. 1 - 15
Keywords
Deep learning
;
diagnostic quality
;
image quality assessment (IQA)
;
knowledge distillation
;
medical image quality
;
no-reference IQA
;
visual perception
Publisher
Institute of Electrical and Electronics Engineers Inc.
Indexed
SCIE; SCOPUS
Document Type
Article
Abstract
An efficient deep-detector image quality assessment (EDIQA) is proposed to address the need for an objective and efficient medical image quality assessment (IQA) without requiring reference images or ground-truth scores from expert radiologists. Existing methods encounter limitations in meeting diagnostic quality and computation efficiency, especially when reference images are unavailable. The proposed EDIQA leverages knowledge distillation in a two-stage training procedure, using a task-based IQA model and the modified deep-detector IQA (mD2IQA) as the teacher model and novel student model designed for effective learning. This approach enables the student model to compute image scores based on a task-based approach without complex signal insertion and multiple predictions, resulting in a speed improvement of over 1.6e+4 times compared to the teacher model. A deep-learning architecture is developed to allow the student model to learn hierarchical multiscale features of the image from low- to high-level semantic features. Rigorous evaluations demonstrate the generalizability of the proposed model across various modalities and anatomical parts, indicating a step toward a universal IQA metric in medical imaging. © 1963-2012 IEEE.
DOI
10.1109/TIM.2023.3346519
Appears in Collections:
인공지능대학
>
인공지능학과
>
Journal papers
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML
Show full item record
Find@EWHA
트윗하기
BROWSE
Communities & Collections
By Date
Authors
Titles
Subject