Interpretable laryngeal tumor grading of histopathological images via depth domain adaptive network with integration gradient CAM and priori experience-guided attention

Author:

Huang PanORCID,Zhou XiaoliORCID,He Peng,Feng Peng,Tian Sukun,Sun Yuchun,Mercaldo Francesco,Santone Antonella,Qin JingORCID,Xiao Hualiang

Funder

National Natural Science Foundation of China

Innovation and Technology Commission

Natural Science Foundation of Chongqing

Innovation and Technology Fund

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference46 articles.

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3. Microscopy image analysis of p63 immunohistochemically stained laryngeal cancer lesions for predicting patient 5-year survival;Ninos;Eur. Arch. Oto-Rhino-Laryngol.,2016

4. Artificial intelligence and laryngeal cancer: from screening to prognosis: a state of the art review;Bensoussan;Otolaryngology-Head Neck Surg. (Tokyo),2022

5. Diagnostic Histopathology of Tumors;Hoda,2021

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