Centralized Space Learning for open-set computer-aided diagnosis

In computer-aided diagnosis (CAD), diagnosing untrained diseases as known categories will cause serious medical accidents, which makes it crucial to distinguish the new class (open set) meanwhile preserving the known classes (closed set) performance so as to enhance the robustness. However, how to accurately define the decision boundary between known and unknown classes is still an open problem, as unknown classes are never seen during the training process, especially in medical area. Moreover, manipulating the latent distribution of known classes further influences the unknown's and makes it even harder. In this paper, we propose the Centralized Space Learning (CSL) method to address the open-set recognition problem in CADs by learning a centralized space to separate the known and unknown classes with the assistance of proxy images generated by a generative adversarial network (GAN). With three steps, including known space initialization, unknown anchor generation and centralized space refinement, CSL learns the optimized space distribution with unknown samples cluster around the center while the known spread away from the center, achieving a significant identification between the known and the unknown. Extensive experiments on multiple datasets and tasks illustrate the proposed CSL's practicability in CAD and the state-of-the-art open-set recognition performance.