TY - GEN
T1 - Data Augmentation with Multi-armed Bandit on Image Deformations Improves Fluorescence Glioma Boundary Recognition
AU - Xiao, Anqi
AU - Han, Keyi
AU - Shi, Xiaojing
AU - Tian, Jie
AU - Hu, Zhenhua
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - The recognition of glioma boundary is challenging as a diffused growthing malignant tumor. Although fluorescence molecular imaging, especially in the second near-infrared window (NIR-II, 1000–1700 nm), helps improve surgical outcomes, fast and precise recognition remains in demand. Data-driven deep learning technology shows great promise in providing objective, fast, and precise recognition for glioma boundaries, but the lack of data poses challenges for designing effective models. Automatic data augmentation can improve the representation of small-scale datasets without requiring extensive prior information, which is suitable for fluorescence-based glioma boundary recognition. We propose Explore and Exploit Augment (EEA) based on multi-armed bandit for image deformations, enabling dynamic policy adjustment during training. Additionally, images captured in white light and the first near-infrared window (NIR-I, 700–900 nm) are introduced to further enhance performance. Experiments demonstrate that EEA improves the generalization of four types of models for glioma boundary recognition, suggesting significant potential for aiding in medical image classification. Code is available at https://github.com/ainieli/EEA.
AB - The recognition of glioma boundary is challenging as a diffused growthing malignant tumor. Although fluorescence molecular imaging, especially in the second near-infrared window (NIR-II, 1000–1700 nm), helps improve surgical outcomes, fast and precise recognition remains in demand. Data-driven deep learning technology shows great promise in providing objective, fast, and precise recognition for glioma boundaries, but the lack of data poses challenges for designing effective models. Automatic data augmentation can improve the representation of small-scale datasets without requiring extensive prior information, which is suitable for fluorescence-based glioma boundary recognition. We propose Explore and Exploit Augment (EEA) based on multi-armed bandit for image deformations, enabling dynamic policy adjustment during training. Additionally, images captured in white light and the first near-infrared window (NIR-I, 700–900 nm) are introduced to further enhance performance. Experiments demonstrate that EEA improves the generalization of four types of models for glioma boundary recognition, suggesting significant potential for aiding in medical image classification. Code is available at https://github.com/ainieli/EEA.
KW - Automatic data augmentation
KW - Glioma
KW - Image classification
KW - Multi-modal imaging
KW - NIR-II fluorescence imaging
UR - https://www.scopus.com/pages/publications/85206455047
U2 - 10.1007/978-3-031-72069-7_13
DO - 10.1007/978-3-031-72069-7_13
M3 - 会议稿件
AN - SCOPUS:85206455047
SN - 9783031720680
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 130
EP - 140
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 - 27th International Conference, Proceedings
A2 - Linguraru, Marius George
A2 - Dou, Qi
A2 - Feragen, Aasa
A2 - Giannarou, Stamatia
A2 - Glocker, Ben
A2 - Lekadir, Karim
A2 - Schnabel, Julia A.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024
Y2 - 6 October 2024 through 10 October 2024
ER -