TY - GEN
T1 - AttenNet
T2 - 26th International Conference on MultiMedia Modeling, MMM 2020
AU - Wu, Jun
AU - Zhang, Yao
AU - Wang, Jie
AU - Zhao, Jianchun
AU - Ding, Dayong
AU - Chen, Ningjiang
AU - Wang, Lingling
AU - Chen, Xuan
AU - Jiang, Chunhui
AU - Zou, Xuan
AU - Liu, Xing
AU - Xiao, Hui
AU - Tian, Yuan
AU - Shang, Zongjiang
AU - Wang, Kaiwei
AU - Li, Xirong
AU - Yang, Gang
AU - Fan, Jianping
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - An optical coherence tomography (OCT) image is becoming the standard imaging modality in diagnosing retinal diseases and the assessment of their progression. However, the manual evaluation of the volumetric scan is time consuming, expensive and the signs of the early disease are easy to miss. In this paper, we mainly present an attention-based deep learning method for the retinal disease classification in OCT images, which can assist the large-scale screening or the diagnosis recommendation for an ophthalmologist. First, according to the unique characteristic of a retinal OCT image, we design a customized pre-processing method to improve image quality. Second, in order to guide the network optimization more effectively, a specially designed attention model, which pays more attention to critical regions containing pathological anomalies, is integrated into a typical deep learning network. We evaluate our proposed method on two data sets, and the results consistently show that it outperforms the state-of-the-art methods. We report an overall four-class accuracy of 97.4%, a two-class sensitivity of 100.0%, and a two-class specificity of 100.0% on a public data set shared by Zhang et al. with 1,000 testing B-scans in four disease classes. Compared to their work, our method improves the numbers by 0.8%, 2.2%, and 2.6% respectively.
AB - An optical coherence tomography (OCT) image is becoming the standard imaging modality in diagnosing retinal diseases and the assessment of their progression. However, the manual evaluation of the volumetric scan is time consuming, expensive and the signs of the early disease are easy to miss. In this paper, we mainly present an attention-based deep learning method for the retinal disease classification in OCT images, which can assist the large-scale screening or the diagnosis recommendation for an ophthalmologist. First, according to the unique characteristic of a retinal OCT image, we design a customized pre-processing method to improve image quality. Second, in order to guide the network optimization more effectively, a specially designed attention model, which pays more attention to critical regions containing pathological anomalies, is integrated into a typical deep learning network. We evaluate our proposed method on two data sets, and the results consistently show that it outperforms the state-of-the-art methods. We report an overall four-class accuracy of 97.4%, a two-class sensitivity of 100.0%, and a two-class specificity of 100.0% on a public data set shared by Zhang et al. with 1,000 testing B-scans in four disease classes. Compared to their work, our method improves the numbers by 0.8%, 2.2%, and 2.6% respectively.
KW - Attention model
KW - Deep learning
KW - Optical Coherence Tomography (OCT)
KW - Retinal disease classification
UR - http://www.scopus.com/inward/record.url?scp=85080925690&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-37734-2_46
DO - 10.1007/978-3-030-37734-2_46
M3 - 会议稿件
AN - SCOPUS:85080925690
SN - 9783030377335
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 565
EP - 576
BT - MultiMedia Modeling - 26th International Conference, MMM 2020, Proceedings
A2 - Ro, Yong Man
A2 - Kim, Junmo
A2 - Choi, Jung-Woo
A2 - Cheng, Wen-Huang
A2 - Chu, Wei-Ta
A2 - Cui, Peng
A2 - Hu, Min-Chun
A2 - De Neve, Wesley
PB - Springer
Y2 - 5 January 2020 through 8 January 2020
ER -