论文标题
半监督关键点检测器和视网膜图像匹配的描述符
Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching
论文作者
论文摘要
对于视网膜图像匹配（RIM），我们提出了SuperRetina，这是一种具有共同训练关键点检测器和描述符的第一个端到端方法。 SuperRetina以一种新颖的半监督方式接受了训练。一小部分（近100张）图像未完全标记，并用于监督网络以检测血管树上的关键点。为了攻击手动标记的不完整性，我们提出了进行性键盘扩展，以丰富每个训练时期的关键点标签。通过利用基于关键点的改进的三重损失作为其描述损失，超级retina可以在全输入图像大小的情况下产生高度歧视性描述符。在多个现实世界数据集上进行了广泛的实验证明了超级丽菌的生存能力。即使手动标记被自动标记取代，从而使训练过程完全免费手动通道，SuperRetina也可以与两个RIM任务（即图像注册和身份验证）的许多强基础进行比较。 SuperRetina将是开源的。
For retinal image matching (RIM), we propose SuperRetina, the first end-to-end method with jointly trainable keypoint detector and descriptor. SuperRetina is trained in a novel semi-supervised manner. A small set of (nearly 100) images are incompletely labeled and used to supervise the network to detect keypoints on the vascular tree. To attack the incompleteness of manual labeling, we propose Progressive Keypoint Expansion to enrich the keypoint labels at each training epoch. By utilizing a keypoint-based improved triplet loss as its description loss, SuperRetina produces highly discriminative descriptors at full input image size. Extensive experiments on multiple real-world datasets justify the viability of SuperRetina. Even with manual labeling replaced by auto labeling and thus making the training process fully manual-annotation free, SuperRetina compares favorably against a number of strong baselines for two RIM tasks, i.e. image registration and identity verification. SuperRetina will be open source.
