Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification
Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification
复制标题
关注病变：用于视网膜光学相干断层扫描图像分类的病变感知卷积神经网络
DOI：
10.1109/tmi.2019.2898414
复制
发表时间：
2019-08-01
影响因子：
10.6
通讯作者：
Liu, Zhimin
中科院分区：
文献类型：
--
作者：
Fang, Leyuan;Wang, Chong;Liu, Zhimin
视网膜光学相干断层扫描（OCT）图像的自动准确分类是辅助眼科医生对黄斑疾病进行诊断和分级的关键。临床上，眼科医生通常根据黄斑病变的结构来诊断黄斑疾病，其形态、大小和数目是重要的标准。在本文中，我们提出了一种新的病变感知卷积神经网络（LACNN）方法用于视网膜OCT图像分类，其中OCT图像中的视网膜病变被用来指导CNN实现更准确的分类。LACNN模拟眼科医生在分析OCT图像时专注于局部病变相关区域的诊断。具体来说，我们首先设计了一个病变检测网络，从整个OCT图像中生成一个软注意力图。然后，将注意力图并入分类网络中，以对局部卷积表示的贡献进行加权。在病变关注图的指导下，分类网络可以利用来自局部病变相关区域的信息来进一步加速网络训练过程并改进OCT分类。我们在两个临床采集的OCT数据集上的实验结果证明了所提出的LACNN方法用于视网膜OCT图像分类的有效性和效率。
Automatic and accurate classification of retinal optical coherence tomography (OCT) images is essential to assist ophthalmologist in the diagnosis and grading of macular diseases. Clinically, ophthalmologists usually diagnose macular diseases according to the structures of macular lesions, whose morphologies, size, and numbers are important criteria. In this paper, we propose a novel lesion-aware convolutional neural network (LACNN) method for retinal OCT image classification, in which retinal lesions within OCT images are utilized to guide the CNN to achieve more accurate classification. The LACNN simulates the ophthalmologists’ diagnosis that focuses on local lesion-related regions when analyzing the OCT image. Specifically, we first design a lesion detection network to generate a soft attention map from the whole OCT image. The attention map is then incorporated into a classification network to weight the contributions of local convolutional representations. Guided by the lesion attention map, the classification network can utilize the information from local lesion-related regions to further accelerate the network training process and improve the OCT classification. Our experimental results on two clinically acquired OCT datasets demonstrate the effectiveness and efficiency of the proposed LACNN method for retinal OCT image classification.