Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching
This addresses retinal image matching for medical imaging applications, but it is incremental as it builds on existing keypoint detection and descriptor methods.
The authors tackled retinal image matching by proposing SuperRetina, the first end-to-end method with jointly trainable keypoint detector and descriptor, trained in a semi-supervised manner using a small set of incompletely labeled images and Progressive Keypoint Expansion, achieving favorable performance against strong baselines for image registration and identity verification tasks.
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.