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Proceedings Paper

From patch-level to ROI-level deep feature representations for breast histopathology classification
Author(s): Caner Mercan; Selim Aksoy; Ezgi Mercan; Linda G. Shapiro; Donald L. Weaver; Joann G. Elmore
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Paper Abstract

We propose a framework for learning feature representations for variable-sized regions of interest (ROIs) in breast histopathology images from the convolutional network properties at patch-level. The proposed method involves fine-tuning a pre-trained convolutional neural network (CNN) by using small fixed-sized patches sampled from the ROIs. The CNN is then used to extract a convolutional feature vector for each patch. The softmax probabilities of a patch, also obtained from the CNN, are used as weights that are separately applied to the feature vector of the patch. The final feature representation of a patch is the concatenation of the class-probability weighted convolutional feature vectors. Finally, the feature representation of the ROI is computed by average pooling of the feature representations of its associated patches. The feature representation of the ROI contains local information from the feature representations of its patches while encoding cues from the class distribution of the patch classification outputs. The experiments show the discriminative power of this representation in a 4-class ROI-level classification task on breast histopathology slides where our method achieved an accuracy of 66.8% on a data set containing 437 ROIs with different sizes.

Paper Details

Date Published: 18 March 2019
PDF: 8 pages
Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 109560H (18 March 2019); doi: 10.1117/12.2510665
Show Author Affiliations
Caner Mercan, Bilkent Univ. (Turkey)
Selim Aksoy, Bilkent Univ. (Turkey)
Ezgi Mercan, Univ. of Washington (United States)
Linda G. Shapiro, Univ. of Washington (United States)
Donald L. Weaver, The Univ. of Vermont (United States)
Joann G. Elmore, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)

Published in SPIE Proceedings Vol. 10956:
Medical Imaging 2019: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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