Evaluating perceptual and semantic interpretability of saliency methods: A case study of melanoma-Reference-Cited by-同舟云学术

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Evaluating perceptual and semantic interpretability of saliency methods: A case study of melanoma

Published:2022-09 Issue:3 Volume:3 Page:
ISSN:2689-5595
Container-title:Applied AI Letters
language:en
Short-container-title:Applied AI Letters

Author:

Bokadia Harshit¹,Yang Scott Cheng‐Hsin¹^ORCID,Li Zhaobin¹,Folke Tomas¹,Shafto Patrick¹²

Affiliation:

1. Department of Mathematics and Computer Science Rutgers University Newark New Jersey USA

2. School of Mathematics Institute for Advanced Study Princeton New Jersey USA

Funder

Air Force Research Laboratory

Defense Advanced Research Projects Agency

National Science Foundation of Sri Lanka

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ail2.77

Reference44 articles.

1. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine

2. Fairness in Criminal Justice Risk Assessments: The State of the Art

3. A Survey of Methods for Explaining Black Box Models

4. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

5. DARPA’s Explainable Artificial Intelligence (XAI) Program

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1. Artificial Intelligence Reveals Distinct Prognostic Subgroups of Muscle-Invasive Bladder Cancer on Histology Images;Cancers;2023-10-16

2. The Inner Loop of Collective Human–Machine Intelligence;Topics in Cognitive Science;2023-02-20

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