Authors:
Ezgi Mercan
1
;
Sachin Mehta
1
;
Jamen Bartlett
2
;
Donald L. Weaver
2
;
Joann G. Elmore
1
and
Linda G. Shapiro
1
Affiliations:
1
University of Washington, United States
;
2
University of Vermont, United States
Keyword(s):
Breast Pathology, Automated Diagnosis, Histopathological Image Analysis.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Classification
;
Computer Vision, Visualization and Computer Graphics
;
Image Understanding
;
Medical Imaging
;
Pattern Recognition
;
Software Engineering
;
Theory and Methods
Abstract:
Digital whole slide imaging has the potential to change diagnostic pathology by enabling the use of computeraided
diagnosis systems. To this end, we used a dataset of 240 digital slides that are interpreted and diagnosed
by an expert panel to develop and evaluate image features for diagnostic classification of breast biopsy whole
slides to four categories: benign, atypia, ductal carcinoma in-situ and invasive carcinoma. Starting with a
tissue labeling step, we developed features that describe the tissue composition of the image and the structural
changes. In this paper, we first introduce two models for the semantic segmentation of the regions of
interest into tissue labels: an SVM-based model and a CNN-based model. Then, we define an image feature
that consists of superpixel tissue label frequency and co-occurrence histograms based on the tissue label segmentations.
Finally, we use our features in two diagnostic classification schemes: a four-class classification,
and an al
ternative classification that is one-diagnosis-at-a-time starting with invasive versus benign and ending
with atypia versus ductal carcinoma in-situ (DCIS). We show that our features achieve competitive results
compared to human performance on the same dataset. Especially at the critical atypia vs. DCIS threshold, our
system outperforms pathologists by achieving an 83% accuracy.
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