A Light-weight Interpretable Compositional Model for Nuclei Detection and Weakly-Supervised Segmentation
This addresses the high annotation effort in histopathology for pathologists, though it is an incremental improvement as it builds on existing weakly-supervised and compositional approaches.
The paper tackles the problem of data-hungry deep learning in computational pathology by proposing a light-weight, interpretable model for nuclei detection and weakly-supervised segmentation, achieving comparable or better detection performance than deep networks with limited annotated data and outperforming other weakly-supervised segmentation methods.
The field of computational pathology has witnessed great advancements since deep neural networks have been widely applied. These networks usually require large numbers of annotated data to train vast parameters. However, it takes significant effort to annotate a large histopathology dataset. We introduce a light-weight and interpretable model for nuclei detection and weakly-supervised segmentation. It only requires annotations on isolated nucleus, rather than on all nuclei in the dataset. Besides, it is a generative compositional model that first locates parts of nucleus, then learns the spatial correlation of the parts to further locate the nucleus. This process brings interpretability in its prediction. Empirical results on an in-house dataset show that in detection, the proposed method achieved comparable or better performance than its deep network counterparts, especially when the annotated data is limited. It also outperforms popular weakly-supervised segmentation methods. The proposed method could be an alternative solution for the data-hungry problem of deep learning methods.