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Paper Abstract and Keywords
Presentation 2022-01-18 11:15
[Invited Talk] TBA
Jun Sakuma (Tsukuba Univ./RIKEN)
Abstract (in Japanese) (See Japanese page) 
(in English) Explainability is one of the key elements required in medical image diagnosis using deep image recognition models. In this talk, the characteristics of explainable AI models, such as pixel attribution, explanation by prototypes, natural language explanation, conceptual explanation, and counterfactual explanation, have been introduced. Then, provide an overview of the necessary technologies for explainable AI from the perspective of which stakeholders will seek explanations and for which purposes.
Keyword (in Japanese) (See Japanese page) 
(in English) medical image diagnosis / explaniable model / AI / explainability / / / /  
Reference Info. IEICE Tech. Rep.
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Conference Information
Committee IBISML  
Conference Date 2022-01-17 - 2022-01-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine Learning, etc. 
Paper Information
Registration To IBISML 
Conference Code 2022-01-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) TBA 
Sub Title (in English)  
Keyword(1) medical image diagnosis  
Keyword(2) explaniable model  
Keyword(3) AI  
Keyword(4) explainability  
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1st Author's Name Jun Sakuma  
1st Author's Affiliation Tsukuba University/RIKEN (Tsukuba Univ./RIKEN)
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Speaker Author-1 
Date Time 2022-01-18 11:15:00 
Presentation Time 40 minutes 
Registration for IBISML 
Paper #  
Volume (vol) vol.121 
Number (no) no.321 
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