Nowadays, predictive medicine begins to become a reality thanks to Artificial Intelligence (AI) which allows, through the processing of huge amounts of data, to identify correlations not perceptible to the human brain. The application of AI in predictive diagnostics is increasingly pervasive; through the use and interpretation of data, the first signs of some diseases (i.e. tumours) can be detected to help physicians make more accurate diagnoses to reduce the errors and develop methods for individualized medical treatment. In this perspective, salivary gland tumours (SGTs) are rare cancers with variable malignancy representing less than 1% of all cancer diagnoses and about 5% of head and neck cancers. The clinical management of SGTs is complicated by a high rate of preclinical diagnostic errors. Today, fine needle aspiration cytology (FNAC) represents the primary diagnostic tool in the hands of clinicians. However, it provides information that about 25% of cases are dubious or inconclusive, complicating therapeutic choices. Thus, finding new tools supporting clinicians to make the right choices in doubtful cases is necessary. This research work presents and discusses a Deep Learning-based framework for automatic segmentation and classification of salivary gland tumours. Furthermore, we propose an explainable segmentation learning approach supporting the effectiveness of the proposed framework through a per-epoch learning process analysis and the attention map mechanism. The proposed framework was evaluated with a collected CT dataset of patients with salivary gland tumours. Experimental results show that our methodology achieves significant scores on both segmentation and classification tasks.

Predictive Medicine for Salivary gland tumours identification through Deep Learning

Prezioso, Edoardo;Izzo, Stefano;Giampaolo, Fabio;Piccialli, Francesco;Dell'Aversana Orabona, Giovanni;Cuocolo, Renato;Abbate, Vincenzo;Ugga, Lorenzo;Califano, Luigi
2021

Abstract

Nowadays, predictive medicine begins to become a reality thanks to Artificial Intelligence (AI) which allows, through the processing of huge amounts of data, to identify correlations not perceptible to the human brain. The application of AI in predictive diagnostics is increasingly pervasive; through the use and interpretation of data, the first signs of some diseases (i.e. tumours) can be detected to help physicians make more accurate diagnoses to reduce the errors and develop methods for individualized medical treatment. In this perspective, salivary gland tumours (SGTs) are rare cancers with variable malignancy representing less than 1% of all cancer diagnoses and about 5% of head and neck cancers. The clinical management of SGTs is complicated by a high rate of preclinical diagnostic errors. Today, fine needle aspiration cytology (FNAC) represents the primary diagnostic tool in the hands of clinicians. However, it provides information that about 25% of cases are dubious or inconclusive, complicating therapeutic choices. Thus, finding new tools supporting clinicians to make the right choices in doubtful cases is necessary. This research work presents and discusses a Deep Learning-based framework for automatic segmentation and classification of salivary gland tumours. Furthermore, we propose an explainable segmentation learning approach supporting the effectiveness of the proposed framework through a per-epoch learning process analysis and the attention map mechanism. The proposed framework was evaluated with a collected CT dataset of patients with salivary gland tumours. Experimental results show that our methodology achieves significant scores on both segmentation and classification tasks.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/861405
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