Abstract
Retinal microaneurysm (MA) detection is essential for diagnosis of diabetic retinopathy (DR) by providing the earliest clinical sign of DR. However, automatically detecting MA has always been a challenge due to the extremely small proportion of MA, the susceptibility to interference from blood vessels, and the obvious contrast difference between MAs. This paper proposed a novel deep learning method to achieve accurate MA detection based on transformation splicing (TS) and multi-context ensemble learning. TS rebalances the proportion of MA and reduces interference from blood vessels by transforming the pixel distribution of each candidate image and reinforcing the features of difficult samples, which enables the subsequent model to better learn the enhanced image features. At the same time, a multi-context ensemble learning combining dual deep learning models and attention mechanism is designed to adaptively learn different spliced image contexts, which improves detection performance for weak MAs. The final scores of the proposed method in e-ophatha-MA, DiaretDB1 and ROC three public datasets are 0.518, 0.429 and 0.306 respectively, which demonstrates the state-of-the-art performance for MA detection.
Published Version
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