Convolution neural network (CNN) is an efficient technique to detect objects in various kinds of images, especially for microaneurysm (MA) of diabetic retinopathy in retinal fundus image. This study proposes a deconvolutional neural network to accurately discriminate MA from non-MA. The deconvolution, instead of pooling operation, is embedded into the CNN to recover the erased details of feature maps of convolutional layers. Three types of images are collected for training and predicting. Furthermore, the extracted features are fed into the fully-connected layers to classify using a softmax layer. Experimental results demonstrate that the proposed method can achieve significant sensitivity and accuracy on multiple public datasets, in comparison to the state-of-the-art. For Retinopathy Online Challenge dataset, the sensitivity and accuracy are improved up to 0.798 and 0.986, respectively.

中文翻译：

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SODNet：使用反卷积神经网络的小目标检测

卷积神经网络（CNN）是一种有效的技术，可以检测各种图像中的对象，特别是对于视网膜底图像中的糖尿病性视网膜病变的微动脉瘤（MA）。这项研究提出了一种反卷积神经网络，以准确地区分MA与非MA。解卷积（而不是合并操作）被嵌入到CNN中，以恢复卷积层特征图的已擦除细节。收集了三种类型的图像以进行训练和预测。此外，将提取的特征输入到完全连接的层中，以使用softmax层进行分类。实验结果表明，与最新技术相比，该方法可以在多个公共数据集上实现显着的灵敏度和准确性。对于视网膜病变在线挑战数据集，