A Light-weight Interpretable CompositionalNetwork for Nuclei Detection and Weakly-supervised Segmentation

• URL: http://arxiv.org/abs/2110.13846v1
• Date: Tue, 26 Oct 2021 16:44:08 GMT
• Title: A Light-weight Interpretable CompositionalNetwork for Nuclei Detection and Weakly-supervised Segmentation
• Authors: Yixiao Zhang, Adam Kortylewski, Qing Liu, Seyoun Park, Benjamin Green, Elizabeth Engle, Guillermo Almodovar, Ryan Walk, Sigfredo Soto-Diaz, Janis Taube, Alex Szalay, and Alan Yuille
• Abstract summary: Deep neural networks usually require large numbers of annotated data to train vast parameters. We propose to build a data-efficient model, which only requires partial annotation, specifically on isolated nucleus.
• Score: 10.196621315018884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The field of computational pathology has witnessed great advancements since deep neural networks have been widely applied. These deep neural networks usually require large numbers of annotated data to train vast parameters. However, it takes significant effort to annotate a large histopathology dataset. We propose to build a data-efficient model, which only requires partial annotation, specifically on isolated nucleus, rather than on the whole slide image. It exploits shallow features as its backbone and is light-weight, therefore a small number of data is sufficient for training. What's more, it is a generative compositional model, which enjoys interpretability in its prediction. The proposed method could be an alternative solution for the data-hungry problem of deep learning methods.

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