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The field of computational pathology has witnessed great advancements sincedeep neural networks have been widely applied. These networks usually requirelarge numbers of annotated data to train vast parameters. However, it takessignificant effort to annotate a large histopathology dataset. We introduce alight-weight and interpretable model for nuclei detection and weakly-supervisedsegmentation. It only requires annotations on isolated nucleus, rather than onall nuclei in the dataset. Besides, it is a generative compositional model thatfirst locates parts of nucleus, then learns the spatial correlation of theparts to further locate the nucleus. This process brings interpretability inits prediction. Empirical results on an in-house dataset show that indetection, the proposed method achieved comparable or better performance thanits deep network counterparts, especially when the annotated data is limited.It also outperforms popular weakly-supervised segmentation methods. Theproposed method could be an alternative solution for the data-hungry problem ofdeep learning methods.

A Light-weight Interpretable Compositional Model for Nuclei Detection and Weakly-Supervised Segmentation