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Abstract #95665 Published in IGR 22-3
MEAL: Meta enhanced Entropy-driven Adversarial Learning for Optic Disc and Cup Segmentation
Ma B; Yang Q; Cui H; Ma JAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2021; 2021: 3273-3276
Accurate segmentation of optic disc (OD) and optic cup (OC) can assist the effective and efficient diagnosis of glaucoma. The domain shift caused by cross-domain data, however, affect the performance of a well-trained model on new datasets from different domain. In order to overcome this problem, we propose a domain adaption model based OD and OC segmentation called Meta enhanced Entropy-driven Adversarial Learning (MEAL). Our segmentation network consists of a meta-enhanced block (MEB) to enhance the adaptability of high-level features, and an attention-based multi-feature fusion (AMF) module for attentive integration of multi-level feature representations. For the optimization, an adversarial cost function driven by entropy map is used to improve the adaptability of the framework. Evaluations and ablation studies on two public fundus image datasets demonstrate the effectiveness of our model, and outstanding performance over other domain adaption methods in comparison.
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