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

TL;DR

This paper presents a lightweight, interpretable model for nuclei detection and weakly-supervised segmentation that requires minimal annotations and performs comparably or better than deep learning methods, especially with limited data.

Contribution

It introduces a generative compositional model that locates nucleus parts and learns their spatial relations, enhancing interpretability and reducing annotation needs.

Findings

Achieves comparable or superior detection performance with limited annotations.

Outperforms popular weakly-supervised segmentation methods.

Provides an interpretable, data-efficient alternative to deep neural networks.

Abstract

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.