Interpretable deep learning approach for oral cancer classification using guided attention inference network

SIGNIFICANCE: Convolutional neural networks (CNNs) show the potential for automated classification of different cancer lesions. However, their lack of interpretability and explainability makes CNNs less than understandable. Furthermore, CNNs may incorrectly concentrate on other areas surrounding the...

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Detalles Bibliográficos
Autores principales: Figueroa, Kevin Chew, Song, Bofan, Sunny, Sumsum, Li, Shaobai, Gurushanth, Keerthi, Mendonca, Pramila, Mukhia, Nirza, Patrick, Sanjana, Gurudath, Shubha, Raghavan, Subhashini, Imchen, Tsusennaro, Leivon, Shirley T., Kolur, Trupti, Shetty, Vivek, Bushan, Vidya, Ramesh, Rohan, Pillai, Vijay, Wilder-Smith, Petra, Sigamani, Alben, Suresh, Amritha, Kuriakose, Moni Abraham, Birur, Praveen, Liang, Rongguang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society of Photo-Optical Instrumentation Engineers 2022
Materias:
General
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8754153/
https://www.ncbi.nlm.nih.gov/pubmed/35023333
http://dx.doi.org/10.1117/1.JBO.27.1.015001

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8754153/
https://www.ncbi.nlm.nih.gov/pubmed/35023333
http://dx.doi.org/10.1117/1.JBO.27.1.015001

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