2023
DOI: 10.1111/his.15067
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Diagnosis with confidence: deep learning for reliable classification of laryngeal dysplasia

Mélanie Lubrano,
Yaëlle Bellahsen‐Harrar,
Sylvain Berlemont
et al.

Abstract: BackgroundDiagnosis of head and neck (HN) squamous dysplasias and carcinomas is critical for patient care, cure, and follow‐up. It can be challenging, especially for grading intraepithelial lesions. Despite recent simplification in the last WHO grading system, the inter‐ and intraobserver variability remains substantial, particularly for nonspecialized pathologists, exhibiting the need for new tools to support pathologists.MethodsIn this study we investigated the potential of deep learning to assist the pathol… Show more

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Cited by 5 publications
(3 citation statements)
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“…However, several other studies have shown the potential benefits of uncertainty estimation in other DL applications in digital pathology. 27,43 Hence, our observed improvement of utilizing SUA compared with uncertainty estimation directly on in-domain data indicates that the SUA approach could improve the obtained benefits from uncertainty estimation in the previously studied scenarios.…”
Section: Discussionmentioning
confidence: 65%
“…However, several other studies have shown the potential benefits of uncertainty estimation in other DL applications in digital pathology. 27,43 Hence, our observed improvement of utilizing SUA compared with uncertainty estimation directly on in-domain data indicates that the SUA approach could improve the obtained benefits from uncertainty estimation in the previously studied scenarios.…”
Section: Discussionmentioning
confidence: 65%
“…Nevertheless, owing to the diversity in both cancer detection targets and AI methodologies, there is a wide variety of these AI tools in the market. 201–208 These targets range from detecting protein biomarkers to cell-free DNA or a combination of these indicators. In crafting AI models, different approaches are adopted, including the use of datasets from case-control studies or databases from real-life cancer screenings.…”
Section: Challenges and Future Perspectivesmentioning
confidence: 99%
“…In Lubrano et al [ 17 ], the authors examined the capability of DL to support the pathologist with automatic and dependable categorization of HI lesions. A huge dataset of HIs (>2000 slides) is planned for emerging as an automatic analytical tool.…”
Section: Literature Workmentioning
confidence: 99%