Lymphoma diagnosis in histopathology using a multi-stage visual learning approach

Noel Codella; Mehdi Moradi; Matt Matasar; Tanveer Sveda-Mahmood; John R. Smith

doi:10.1117/12.2217158

23 March 2016 Lymphoma diagnosis in histopathology using a multi-stage visual learning approach

Noel Codella, Mehdi Moradi, Matt Matasar, Tanveer Sveda-Mahmood, John R. Smith

Proceedings Volume 9791, Medical Imaging 2016: Digital Pathology; 97910H (2016) https://doi.org/10.1117/12.2217158
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

This work evaluates the performance of a multi-stage image enhancement, segmentation, and classification approach for lymphoma recognition in hematoxylin and eosin (H and E) stained histopathology slides of excised human lymph node tissue. In the first stage, the original histology slide undergoes various image enhancement and segmentation operations, creating an additional 5 images for every slide. These new images emphasize unique aspects of the original slide, including dominant staining, staining segmentations, non-cellular groupings, and cellular groupings. For the resulting 6 total images, a collection of visual features are extracted from 3 different spatial configurations. Visual features include the first fully connected layer (4096 dimensions) of the Caffe convolutional neural network trained from ImageNet data. In total, over 200 resultant visual descriptors are extracted for each slide. Non-linear SVMs are trained over each of the over 200 descriptors, which are then input to a forward stepwise ensemble selection that optimizes a late fusion sum of logistically normalized model outputs using local hill climbing. The approach is evaluated on a public NIH dataset containing 374 images representing 3 lymphoma conditions: chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL). Results demonstrate a 38.4% reduction in residual error over the current state-of-art on this dataset.

Citation Download Citation

Noel Codella, Mehdi Moradi, Matt Matasar, Tanveer Sveda-Mahmood, and John R. Smith "Lymphoma diagnosis in histopathology using a multi-stage visual learning approach", Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97910H (23 March 2016); https://doi.org/10.1117/12.2217158

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

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.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available