Diagnosing oral and maxillofacial diseases using deep learning

• Published in: Scientific reports Vol. 14; no. 1; pp. 2497 - 14
• Main Authors: Kang, Junegyu, Le, Van Nhat Thang, Lee, Dae-Woo, Kim, Sungchan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 30-01-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 692/699/3020; 692/700/139; 692/700/1421/1770; 692/700/3032; Classification; Deep learning; Humanities and Social Sciences; Jaw; Lesions; Localization; Maxillofacial; multidisciplinary; Neural networks; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-52929-0

The classification and localization of odontogenic lesions from panoramic radiographs is a challenging task due to the positional biases and class imbalances of the lesions. To address these challenges, a novel neural network,
DOLNet
, is proposed that uses mutually influencing hierarchical attention across different image scales to jointly learn the
global
representation of the entire jaw and the
local
discrepancy between normal tissue and lesions. The proposed approach uses local attention to learn representations within a patch. From the patch-level representations, we generate inter-patch, i.e., global, attention maps to represent the positional prior of lesions in the whole image. Global attention enables the reciprocal calibration of path-level representations by considering non-local information from other patches, thereby improving the generation of whole-image-level representation. To address class imbalances, we propose an effective data augmentation technique that involves merging lesion crops with normal images, thereby synthesizing new abnormal cases for effective model training. Our approach outperforms recent studies, enhancing the classification performance by up to 42.4% and 44.2% in recall and F1 scores, respectively, and ensuring robust lesion localization with respect to lesion size variations and positional biases. Our approach further outperforms human expert clinicians in classification by 10.7 % and 10.8 % in recall and F1 score, respectively.