TY - GEN
T1 - Unsupervised Lesion-Aware Transfer Learning for Diabetic Retinopathy Grading in Ultra-Wide-Field Fundus Photography
AU - Bai, Yanmiao
AU - Hao, Jinkui
AU - Fu, Huazhu
AU - Hu, Yan
AU - Ge, Xinting
AU - Liu, Jiang
AU - Zhao, Yitian
AU - Zhang, Jiong
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Ultra-wide-field (UWF) fundus photography is a new imaging technique with providing a broader field of view images, and it has become a popular and effective tool for the screening and diagnosis for many eye diseases, such as diabetic retinopathy (DR). However, it is practically challenging to train a robust deep learning model for DR grading in UWF images, due to the limited scale of data and manual annotations. By contrast, we may find large-scale high-quality regular color fundus photography datasets in the research community, with either image-level or pixel-level annotation. In consequence, we propose an Unsupervised Lesion-aware TRAnsfer learning framework (ULTRA) for DR grading in UWF images, by leveraging a large amount of publicly well-annotated regular color fundus images. Inspired by the clinical identification of DR severity, i.e., the decision making process of ophthalmologists based on the type and number of associated lesions, we design an adversarial lesion map generator to provide the auxiliary lesion information for DR grading. A Lesion External Attention Module (LEAM) is introduced to integrate the lesion feature into the model, allowing a relative explainable DR grading. Extensive experimental results show the proposed method is superior to the state-of-the-art methods.
AB - Ultra-wide-field (UWF) fundus photography is a new imaging technique with providing a broader field of view images, and it has become a popular and effective tool for the screening and diagnosis for many eye diseases, such as diabetic retinopathy (DR). However, it is practically challenging to train a robust deep learning model for DR grading in UWF images, due to the limited scale of data and manual annotations. By contrast, we may find large-scale high-quality regular color fundus photography datasets in the research community, with either image-level or pixel-level annotation. In consequence, we propose an Unsupervised Lesion-aware TRAnsfer learning framework (ULTRA) for DR grading in UWF images, by leveraging a large amount of publicly well-annotated regular color fundus images. Inspired by the clinical identification of DR severity, i.e., the decision making process of ophthalmologists based on the type and number of associated lesions, we design an adversarial lesion map generator to provide the auxiliary lesion information for DR grading. A Lesion External Attention Module (LEAM) is introduced to integrate the lesion feature into the model, allowing a relative explainable DR grading. Extensive experimental results show the proposed method is superior to the state-of-the-art methods.
KW - Diabetic retinopathy
KW - UWF imaging
KW - Unsupervised
UR - http://www.scopus.com/inward/record.url?scp=85139043607&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-16434-7_54
DO - 10.1007/978-3-031-16434-7_54
M3 - Conference contribution
AN - SCOPUS:85139043607
SN - 9783031164330
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 560
EP - 570
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
A2 - Wang, Linwei
A2 - Dou, Qi
A2 - Fletcher, P. Thomas
A2 - Speidel, Stefanie
A2 - Li, Shuo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Y2 - 18 September 2022 through 22 September 2022
ER -
