Interpretable and Reliable Oral Cancer Classifier with Attention Mechanism and Expert Knowledge Embedding via Attention Map

SIMPLE SUMMARY: Convolutional neural networks (CNNs) have shown promising performance in recognizing oral cancer. However, the lack of interpretability and reliability remain major challenges in the development of trustworthy computer-aided diagnosis systems. To address this issue, we proposed a neu...

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Detalles Bibliográficos
Autores principales: Song, Bofan, Zhang, Chicheng, Sunny, Sumsum, KC, Dharma Raj, Li, Shaobai, Gurushanth, Keerthi, Mendonca, Pramila, Mukhia, Nirza, Patrick, Sanjana, Gurudath, Shubha, Raghavan, Subhashini, Tsusennaro, Imchen, Leivon, Shirley T., Kolur, Trupti, Shetty, Vivek, Bushan, Vidya, Ramesh, Rohan, Pillai, Vijay, Wilder-Smith, Petra, Suresh, Amritha, Kuriakose, Moni Abraham, Birur, Praveen, Liang, Rongguang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10001266/
https://www.ncbi.nlm.nih.gov/pubmed/36900210
http://dx.doi.org/10.3390/cancers15051421

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10001266/
https://www.ncbi.nlm.nih.gov/pubmed/36900210
http://dx.doi.org/10.3390/cancers15051421

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