Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigation

Autor: Rebecca S Stone, Nishant Ravikumar, Andrew J Bulpitt, David C Hogg

Deep neural networks are highly susceptible to learning biases in visual data. While various methods have been proposed to mitigate such bias, the majority require explicit knowledge of the biases present in the training data in order to mitigate. We

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bdbc5e67be52c1789459dd52338c7777

3D reconstruction of multiple stained histology images

Autor: Yi Song, Darren Treanor, Andrew J Bulpitt, Derek R Magee

Publikováno v: Journal of Pathology Informatics, Vol 4, Iss 2, Pp 7-7 (2013)

Context: Three dimensional (3D) tissue reconstructions from the histology images with different stains allows the spatial alignment of structural and functional elements highlighted by different stains for quantitative study of many physiological and

Externí odkaz: https://doaj.org/article/b074dd60dce742e19ebb4ead87aa7f0d

First Extended Catalogue of Galactic Bubbles InfraRed Fluxes from WISE and Herschel Surveys

Autor: Filomena Bufano, F. Cavallaro, D. Carey, A. Ingallinera, Simone Riggi, Paolo Leto, Carla Buemi, Andrew J. Bulpitt, Sergio Molinari, Corrado Trigilio, G. Umana

In this paper, we present the first extended catalogue of far-infrared fluxes of Galactic bubbles. Fluxes were estimated for 1814 bubbles, defined here as the `golden sample', and were selected from the Milky Way Project First Data Release (Simpson e

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f95783ca0367285f36f42181d81ca851
http://arxiv.org/abs/1711.06263

Interventional radiology virtual simulator for liver biopsy

Autor: Andrew J. Bulpitt, Derek Gould, S Pisharody, Franck Vidal, Fernando Bello, Richard Holbrey, Pierre-Frédéric Villard, Nigel W. John, L ap Cenydd, Sheena Johnson

Publikováno v: International Journal of Computer Assisted Radiology and Surgery
International Journal of Computer Assisted Radiology and Surgery, Springer Verlag, 2013, pp.1-13. ⟨10.1007/s11548-013-0929-0⟩
International Journal of Computer Assisted Radiology and Surgery, 2013, pp.1-13. ⟨10.1007/s11548-013-0929-0⟩

International audience; Purpose Training in Interventional Radiology currently uses the apprenticeship model, where clinical and technical skills of invasive procedures are learnt during practice in patients. This apprenticeship training method is in

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b87f9b3cef6ff67f37a3dab6ba791768
https://doi.org/10.1007/s11548-013-0929-0

GO-At :in silicoprediction of gene function inArabidopsis thalianaby combining heterogeneous data

Autor: David R. Westhead, Chris J. Needham, Matthew A. Care, James R. Bradford, Philip Tedder, Andrew J. Bulpitt

Publikováno v: The Plant Journal. 61:713-721

*† ‡ SUMMARY Despite recent advances, accurate gene function prediction remains an elusive goal, with very few methods directly applicable to the plant Arabidopsis thaliana. In this study, we present GO-At (gene ontology prediction in A. thaliana

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c7015d7a0fc311bc0ffcbbfb802af8e0
https://doi.org/10.1111/j.1365-313x.2009.04097.x