T-Net: Hierarchical Pyramid Network for Microaneurysm Detection in Retinal Fundus Image

The mainstream techniques to detect microaneurysm (MA) in medical images involve the use of stacked convolutional neural networks (SCNNs). But these techniques cannot provide adequate detecting performance for the MA scenarios with limited information and different sizes. In this article, a hierarchical pyramid network with a "T" structure is proposed to detect MAs in retinal fundus images, which can overcome the difficulties caused by the above scenarios. We design a data preparation (DP) technique that can adaptively compute the best receptive fields of MAs to generate various patch sizes, resulting in multisize datasets for training. The "T" network consists of two pyramid feature extractors, rather than a deep network to adequately extract features of MAs and avoid feature loss caused by deep layers. Experiments on five public retinal datasets demonstrate that our method achieves the state-of-the-art performance.