A Deep-Learning Algorithm for Thyroid Malignancy Prediction From Whole Slide Cytopathology Images

Dov, David

Kovalsky, Shahar Z

Assaad, Serge

Cohen, Jonathan

Range, Danielle Elliott

Pendse, Avani A

Henao, Ricardo

Carin, Lawrence

2019-05-02T04:26:58Z

2019-05-02T04:26:58Z

2019-05-02T04:26:56Z

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.

https://hdl.handle.net/10161/18529

Elsevier BV

physics.med-ph

physics.med-ph

cs.CV

A Deep-Learning Algorithm for Thyroid Malignancy Prediction From Whole Slide Cytopathology Images

Journal article

Pendse, Avani A|0000-0002-9243-8047

Henao, Ricardo|0000-0003-4980-845X

Trinity College of Arts & Sciences

Duke

Mathematics