Explainability of artificial intelligence methods, applications and challenges: A comprehensive survey
W Ding, M Abdel-Basset, H Hawash, AM Ali - Information Sciences, 2022 - Elsevier
The continuous advancement of Artificial Intelligence (AI) has been revolutionizing the
strategy of decision-making in different life domains. Regardless of this achievement, AI …
strategy of decision-making in different life domains. Regardless of this achievement, AI …
Interpretability research of deep learning: A literature survey
B Xua, G Yang - Information Fusion, 2024 - Elsevier
Deep learning (DL) has been widely used in various fields. However, its black-box nature
limits people's understanding and trust in its decision-making process. Therefore, it becomes …
limits people's understanding and trust in its decision-making process. Therefore, it becomes …
[HTML][HTML] Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …
applications, but the outcomes of many AI models are challenging to comprehend and trust …
Jcs: An explainable covid-19 diagnosis system by joint classification and segmentation
Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in
over 200 countries, influencing billions of humans. To control the infection, identifying and …
over 200 countries, influencing billions of humans. To control the infection, identifying and …
C-cam: Causal cam for weakly supervised semantic segmentation on medical image
Recently, many excellent weakly supervised semantic segmentation (WSSS) works are
proposed based on class activation map** (CAM). However, there are few works that …
proposed based on class activation map** (CAM). However, there are few works that …
Expansion and shrinkage of localization for weakly-supervised semantic segmentation
Generating precise class-aware pseudo ground-truths, aka, class activation maps (CAMs), is
essential for Weakly-Supervised Semantic Segmentation. The original CAM method usually …
essential for Weakly-Supervised Semantic Segmentation. The original CAM method usually …
Extracting class activation maps from non-discriminative features as well
Extracting class activation maps (CAM) from a classification model often results in poor
coverage on foreground objects, ie, only the discriminative region (eg, the" head" of" sheep") …
coverage on foreground objects, ie, only the discriminative region (eg, the" head" of" sheep") …
[HTML][HTML] Quod erat demonstrandum?-Towards a typology of the concept of explanation for the design of explainable AI
In this paper, we present a fundamental framework for defining different types of
explanations of AI systems and the criteria for evaluating their quality. Starting from a …
explanations of AI systems and the criteria for evaluating their quality. Starting from a …
Towards better understanding attribution methods
Deep neural networks are very successful on many vision tasks, but hard to interpret due to
their black box nature. To overcome this, various post-hoc attribution methods have been …
their black box nature. To overcome this, various post-hoc attribution methods have been …
Endoscopic image classification based on explainable deep learning
Deep learning has achieved remarkably positive results and impacts on medical diagnostics
in recent years. Due to its use in several proposals, deep learning has reached sufficient …
in recent years. Due to its use in several proposals, deep learning has reached sufficient …