A structured latent model for ovarian carcinoma subtyping from histopathology slides

Medical Image Analysis
Aïcha BenTaiebGhassan Hamarneh

Abstract

Accurate subtyping of ovarian carcinomas is an increasingly critical and often challenging diagnostic process. This work focuses on the development of an automatic classification model for ovarian carcinoma subtyping. Specifically, we present a novel clinically inspired contextual model for histopathology image subtyping of ovarian carcinomas. A whole slide image is modelled using a collection of tissue patches extracted at multiple magnifications. An efficient and effective feature learning strategy is used for feature representation of a tissue patch. The locations of salient, discriminative tissue regions are treated as latent variables allowing the model to explicitly ignore portions of the large tissue section that are unimportant for classification. These latent variables are considered in a structured formulation to model the contextual information represented from the multi-magnification analysis of tissues. A novel, structured latent support vector machine formulation is defined and used to combine information from multiple magnifications while simultaneously operating within the latent variable framework. The structural and contextual nature of our method addresses the challenges of intra-class variation and pathologi...Continue Reading

Citations

Nov 9, 2019·Biomolecules·Vanessa Rosse de SouzaAnderson Junger Teodoro
Sep 5, 2020·Modern Pathology : an Official Journal of the United States and Canadian Academy of Pathology, Inc·Stephanie A HarmonTamara Jamaspishvili

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