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

Authors: Codella, N.; Moradi, M.; Matasar, M.; Sveda-Mahmood, T.; Smith, J. R.
Editors: Gurcan, M. N.; Madabhushi, A.
Title: Lymphoma diagnosis in histopathology using a multi-stage visual learning approach
Conference Title: Medical Imaging 2016: Digital Pathology
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. © 2016 SPIE.
Keywords: leukemia; classification; pathology; oncology; image enhancement; medical imaging; diagnosis; lymphoma; segmentation; follicular lymphoma; diseases; image segmentation; classification (of information); neural networks; classification approach; convolutional neural network; histopatholgy; chronic lymphocytic leukemias; spatial configuration
Journal Title Proceedings of SPIE
Volume: 9791
Conference Dates: 2016 Feb 27-Mar 3
Conference Location: San Diego, CA
ISBN: 0277-786X
Publisher: SPIE  
Date Published: 2016-01-01
Start Page: 97910H
Language: English
DOI: 10.1117/12.2217158
PROVIDER: scopus
Notes: Conference Paper -- Conference code: 123863 -- Export Date: 2 November 2016 -- Source: Scopus
Citation Impact
MSK Authors
  1. Matthew J Matasar
    244 Matasar