Integrating Eye-Gaze Data into CXR DL Approaches: A Preliminary study

Luís, A; Hsieh, C; Nobre, IB; Sousa, SC; Maciel, A; Jorge, J; Moreira, C

Integrating Eye-Gaze Data into CXR DL Approaches: A Preliminary study

Luís, A Hsieh, C Nobre, IB Sousa, SC Maciel, A Jorge, J Moreira, C

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), 2023, 00, pp. 196-199
Issue Date:: 2023-05-01

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Field	Value	Language
dc.contributor.author	Luís, A
dc.contributor.author	Hsieh, C
dc.contributor.author	Nobre, IB
dc.contributor.author	Sousa, SC
dc.contributor.author	Maciel, A
dc.contributor.author	Jorge, J
dc.contributor.author	Moreira, C
dc.date	2023-03-25
dc.date.accessioned	2024-05-09T05:05:33Z
dc.date.available	2024-05-09T05:05:33Z
dc.date.issued	2023-05-01
dc.identifier.citation	2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), 2023, 00, pp. 196-199
dc.identifier.isbn	9798350348392
dc.identifier.uri	http://hdl.handle.net/10453/178764
dc.description.abstract	This paper proposes a novel multimodal DL architecture incorporating medical images and eye tracking data for abnormality detection in chest x rays Our results show that applying eye gaze data directly into DL architectures does not show superior predictive performance in abnormality detection chest X rays These results support other works in the literature and suggest that human generated data such as eye gaze needs a more thorough investigation before being applied to DL architectures
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)
dc.relation.ispartof	2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops
dc.relation.isbasedon	10.1109/vrw58643.2023.00048
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Integrating Eye-Gaze Data into CXR DL Approaches: A Preliminary study
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Shanghai, China
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Provost
utslib.copyright.status	closed_access	*
dc.date.updated	2024-05-09T05:05:32Z
pubs.finish-date	2023-03-29
pubs.publication-status	Published
pubs.start-date	2023-03-25
pubs.volume	00

Abstract:

This paper proposes a novel multimodal DL architecture incorporating medical images and eye tracking data for abnormality detection in chest x rays Our results show that applying eye gaze data directly into DL architectures does not show superior predictive performance in abnormality detection chest X rays These results support other works in the literature and suggest that human generated data such as eye gaze needs a more thorough investigation before being applied to DL architectures

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http://hdl.handle.net/10453/178764