In this work, we introduce a tensor-based computation and modeling framework for the analysis of digital pathology images at different resolutions. We represent digital pathology images as a third-order tensor (a three-way array) with modes: images, features and scales, by extracting features at different scales. The constructed tensor is then analyzed using the most popular tensor factorization methods, i.e., CANDECOMP/PARAFAC and Tucker. These tensor models enable us to extract the underlying patterns in each mode (i.e. images, features and scales) and examine how these patterns are related to each other. As a motivating example, we analyzed 500 follicular lymphoma images corresponding to high power fields, evaluated by three expert hematopathologists. Numerical experiments demonstrate that (i) tensor models capture easily-interpretable patterns showing the significant features and scales, and (ii) patterns extracted by the right tensor model, which in this case is the Tucker model commonly used for exploratory analysis of higher-order tensors, perform as well as the reduced dimensions captured by matrix factorization methods on unfolded data, in terms of follicular lymphoma grading.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.