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Sophia Bano

Researcher at University College London

Publications -  52
Citations -  471

Sophia Bano is an academic researcher from University College London. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 7, co-authored 33 publications receiving 188 citations. Previous affiliations of Sophia Bano include University of Dundee & Queen Mary University of London.

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Journal ArticleDOI

Biomedical image analysis competitions: The state of current participation practice

Matthias Eisenmann, +353 more
- 16 Dec 2022 - 
TL;DR: In this paper , only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%), while 48% of respondents applied postprocessing steps.
Journal ArticleDOI

Intraoperative colon perfusion assessment using multispectral imaging

TL;DR: A multispectral imaging (MSI) laparoscope that can derive quantitative measures of tissue oxygen saturation (SO2) is described in this article. But it is not suitable for the use in colorectal anastomosis, as it may result in a leak and consequent morbidity.
Journal ArticleDOI

Large-scale surgical workflow segmentation for laparoscopic sacrocolpopexy

TL;DR: In this article , sequence-to-sequence (seq2seq) models for coarse-level phase segmentation were proposed to deal with highly variable phase durations in Laparoscopic sacrocolpopexy.
Proceedings ArticleDOI

Finding Time Together: Detection and Classification of Focused Interaction in Egocentric Video

TL;DR: Empirical evidence is provided that fusion of visual face track scores, camera motion profile and audio voice activity scores is an effective combination for focused interaction classification of unconstrained egocentric videos.
Journal ArticleDOI

DeSmoke-LAP: improved unpaired image-to-image translation for desmoking in laparoscopic surgery

TL;DR: DeSmoke-LAP as discussed by the authors is based on the unpaired image-to-image cycle-consistent generative adversarial network in which two novel loss functions, namely, inter-channel discrepancies and dark channel prior, are integrated to facilitate smoke removal.