A Deep-Learning Algorithm for Thyroid Malignancy Prediction From Whole Slide Cytopathology Images
Abstract
We consider thyroid-malignancy prediction from ultra-high-resolution
whole-slide cytopathology images. We propose a deep-learning-based algorithm
that is inspired by the way a cytopathologist diagnoses the slides. The
algorithm identifies diagnostically relevant image regions and assigns them
local malignancy scores, that in turn are incorporated into a global malignancy
prediction. We discuss the relation of our deep-learning-based approach to
multiple-instance learning (MIL) and describe how it deviates from classical
MIL methods by the use of a supervised procedure to extract relevant regions
from the whole-slide. The analysis of our algorithm further reveals a close
relation to hypothesis testing, which, along with unique characteristics of
thyroid cytopathology, allows us to devise an improved training strategy. We
further propose an ordinal regression framework for the simultaneous prediction
of thyroid malignancy and an ordered diagnostic score acting as a regularizer,
which further improves the predictions of the network. Experimental results
demonstrate that the proposed algorithm outperforms several competing methods,
achieving performance comparable to human experts.
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https://hdl.handle.net/10161/18529Collections
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Show full item recordScholars@Duke
Lawrence Carin
Professor of Electrical and Computer Engineering
Lawrence Carin earned the BS, MS, and PhD degrees in electrical engineering at the
University of Maryland, College Park, in 1985, 1986, and 1989, respectively. In 1989
he joined the Electrical Engineering Department at Polytechnic University (Brooklyn)
as an Assistant Professor, and became an Associate Professor there in 1994. In September
1995 he joined the Electrical and Computer Engineering (ECE) Department at Duke University,
where he is now a Professor. He was ECE Department Chair from 2011
Dov David
Adjunct Associate in the Department of Pathology
Ricardo Henao
Associate Professor in Biostatistics & Bioinformatics
Shahar Kovalsky
Phillip Griffiths Assistant Research Professor
Avani Anil Pendse
Associate Professor of Pathology
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