Generation of Textual Explanations in XAI: the Case of Semantic Annotation

Poli, Jean-Philippe; Ouerdane, Wassila; Pierrard, Régis

doi:10.1109/fuzz45933.2021.9494589

Cited by 7 publications

(7 citation statements)

References 22 publications

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“…The preliminary work of Panigutti et al [PPP20] uses ontologies that label the input data, but we expect future works to use input datasets with no ontology annotations. Poli et al [POP21] presented a sentence generation system for image segmentation. Such an approach can be adapted to similar contexts relying on DL.…”

Section: Make the Xdl Systems More Trustworthy Thanks To Vamentioning

confidence: 99%

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State of the Art of Visual Analytics for eXplainable Deep Learning

Rosa

Blasilli

Bourqui

et al. 2023

Computer Graphics Forum

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The use and creation of machine-learning-based solutions to solve problems or reduce their computational costs are becoming increasingly widespread in many domains. Deep Learning plays a large part in this growth. However, it has drawbacks such as a lack of explainability and behaving as a black-box model. During the last few years, Visual Analytics has provided several proposals to cope with these drawbacks, supporting the emerging eXplainable Deep Learning field. This survey aims to (i) systematically report the contributions of Visual Analytics for eXplainable Deep Learning; (ii) spot gaps and challenges; (iii) serve as an anthology of visual analytical solutions ready to be exploited and put into operation by the Deep Learning community (architects, trainers and end users) and (iv) prove the degree of maturity, ease of integration and results for specific domains. The survey concludes by identifying future research challenges and bridging activities that are helpful to strengthen the role of Visual Analytics as effective support for eXplainable Deep Learning and to foster the adoption of Visual Analytics solutions in the eXplainable Deep Learning community. An interactive explorable version of this survey is available online at https:// awarediag-sapienza.github.io/ VA4XDL.

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Section: Make the Xdl Systems More Trustworthy Thanks To Vamentioning

confidence: 99%

“…Poli et al. [POP21] presented a sentence generation system for image segmentation. Such an approach can be adapted to similar contexts relying on DL.…”

Section: Research Challengesmentioning

confidence: 99%

State of the Art of Visual Analytics for eXplainable Deep Learning

Rosa

Blasilli

Bourqui

et al. 2023

Computer Graphics Forum

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“…Traditional Chinese Medical constitutional types as the basic sample data, the fuzzy linguistic variables are represented using membership degree in neural network model (Li, Wei et al 2021). Moreover, semantic image annotation with fuzzy logic is a useful technique that can capture not only imprecise segmented images but also vague human spatial knowledge and vocabulary (Poli, Ouerdane et al 2021).…”

Section: Natural Language Processingmentioning

confidence: 99%

A Systematic Survey of Fuzzy Deep Learning for Uncertain Medical Data

Zheng

Zhang

et al. 2023

Preprint

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Intelligent medical industry is in a rapid stage of development around the world, followed by are the expanding market size and basic theories of intelligent medical diagnosis and decision-making. Deep learning models have achieved good practical results in medical domain. However, traditional deep learning is almost calculated and developed by crisp values, while imprecise, uncertain, and vague medical data is common in the process of diagnosis and treatment. Medical data is usually uncertain because of the presence of noise, artifact or high dimensional unstructured information in the data. Fortunately, fuzzy deep learning that originated from fuzzy sets, can effectively deal with uncertain and inaccurate information, providing new viewpoints for alleviating the problems above. Therefore, it is important and necessary to review the contributions of fuzzy deep learning for uncertain medical data in recent years. This paper first constructs four types of frameworks of fuzzy deep learning methods used for uncertain medical data, and investigates the status from three aspects: fuzzy deep learning methods, type of uncertain medical data and execution effects. Then the performance evaluation metrics of fuzzy deep learning models are analyzed in details. We have found that although fuzzy deep learning indeed develops the interpretability, low-quality images, boundary point processing, imprecise text records processing and multi-source heterogenous data fusion in medical domain, challenges still exist and future research directions are provided from the perspective of genetic data processing, intelligent medical decision-making based on complex cognition information, health management of Traditional Chinese Medical and few-shot learning.

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“…One of the most common methods of providing explanations is through text [5,24]. Textual explanations consist of brief sections of text that explain the rationale of the XAI model.…”

Section: Related Workmentioning

confidence: 99%

A Co-design Study for Multi-stakeholder Job Recommender System Explanations

Schellingerhout,

Barile,

Tintarev

2023

Communications in Computer and Information Science

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Generation of Textual Explanations in XAI: the Case of Semantic Annotation

Cited by 7 publications

References 22 publications

State of the Art of Visual Analytics for eXplainable Deep Learning

State of the Art of Visual Analytics for eXplainable Deep Learning

A Systematic Survey of Fuzzy Deep Learning for Uncertain Medical Data

A Co-design Study for Multi-stakeholder Job Recommender System Explanations

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