Tensor-based computation and modeling in multi-resolution digital pathology imaging: application to follicular lymphoma grading

TY - GEN

T1 - Tensor-based computation and modeling in multi-resolution digital pathology imaging

T2 - application to follicular lymphoma grading

AU - Evrim, Acar Ataman

AU - Lozanski, Gerard

AU - Gurcan, Metin

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

U2 - 10.1117/12.2006025

DO - 10.1117/12.2006025

M3 - Conference article

SN - 0277-786X

VL - 8676

JO - Proceedings of S P I E - International Society for Optical Engineering

JF - Proceedings of S P I E - International Society for Optical Engineering

M1 - 867603

ER -