Prediction of Thyroid Malignancy Using Contextual Semantic Interpretability from Sonograms

Ahana Choudhury, Radu Mihail, Sorin Chiriac

2023

Abstract

The gold standard in thyroid nodule malignancy diagnosis consists of ultrasound (US or sonogram) guided fine needle aspiration biopsy. This procedure is ordered based on an assessment of malignancy risk by a trained radiologist, who uses US images and relies on experience and heuristics that are difficult to effectively systematize into a working algorithm. Artificial Intelligence (AI) methods for malignancy detection in sonograms are designed to either perform segmentation (highlight entire thyroid gland and/or nodule) or output a probability of malignancy. There is a gap between AI methods trained to perform a specific task using a black-box method, and the sonogram features (e.g.,: shape, size, echogenicity, echotexture) that a radiologist looks at. We aim to bridge this gap, using AI to reveal saliency in sonograms for features that are easily understood by clinicians. We propose a deep-learning model that performs two tasks important to radiologists: sonogram feature saliency detection, as well as probability of malignancy. We perform both a quantitative and qualitative evaluation of our method using an open dataset, the Thyroid Digital Image Database (TDID). Our framework achieves 72% accuracy in the task of classifying thyroid nodules as benign or malignant.

Paper Citation

in Harvard Style

Choudhury A., Mihail R. and Chiriac S. (2023). Prediction of Thyroid Malignancy Using Contextual Semantic Interpretability from Sonograms. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 2: BIOIMAGING; ISBN 978-989-758-631-6, SciTePress, pages 94-101. DOI: 10.5220/0011670900003414

in Bibtex Style

@conference{bioimaging23,
author={Ahana Choudhury and Radu Mihail and Sorin Chiriac},
title={Prediction of Thyroid Malignancy Using Contextual Semantic Interpretability from Sonograms},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 2: BIOIMAGING},
year={2023},
pages={94-101},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011670900003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 2: BIOIMAGING
TI - Prediction of Thyroid Malignancy Using Contextual Semantic Interpretability from Sonograms
SN - 978-989-758-631-6
AU - Choudhury A.
AU - Mihail R.
AU - Chiriac S.
PY - 2023
SP - 94
EP - 101
DO - 10.5220/0011670900003414
PB - SciTePress