Incremental Nuclei Segmentation from Histopathological Images via Future-class Awareness and Compatibility-inspired Distillation

We present a novel semantic segmentation approach for incremental nuclei segmentation from histopathological images, which is a very challenging task as we have to incrementally optimize existing models to make them perform well in both old and new classes without using training samples of old classes. Yet, it is an indispensable component of computer-aided diagnosis systems. The proposed approach has two key techniques. First, we propose a new future-class awareness mechanism by separating some potential regions for future classes from background based on their similarities to both old and new classes in the representation space. With this mechanism, we can not only reserve more parameter space for future updates but also enhance the representation capability of learned features. We further propose an innovative compatibility-inspired distillation scheme to make our model take full advantage of the knowledge learned by the old model. We conducted extensive experiments on two famous histopathological datasets and the results demonstrate the proposed approach achieves much better performance than state-of-the-art approaches. The code is available at https://github.com/why19991/InSeg.