Imperial College London
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ProfessorAndreaRockall

Faculty of Medicine, Department of Surgery & Cancer

Clinical Chair in Radiology
 
 
 
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Contact

 

a.rockall

 
 
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Location

 

ICTEM buildingHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Valindria:2018,
author = {Valindria, VV and Lavdas, I and Bai, W and Kamnitsas, K and Aboagye, EO and Rockall, AG and Rueckert, D and Glocker, B},
title = {Domain adaptation for MRI organ segmentation using reverse classification accuracy},
url = {http://arxiv.org/abs/1806.00363v1},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - The variations in multi-center data in medical imaging studies have broughtthe necessity of domain adaptation. Despite the advancement of machine learningin automatic segmentation, performance often degrades when algorithms areapplied on new data acquired from different scanners or sequences than thetraining data. Manual annotation is costly and time consuming if it has to becarried out for every new target domain. In this work, we investigate automaticselection of suitable subjects to be annotated for supervised domain adaptationusing the concept of reverse classification accuracy (RCA). RCA predicts theperformance of a trained model on data from the new domain and differentstrategies of selecting subjects to be included in the adaptation via transferlearning are evaluated. We perform experiments on a two-center MR database forthe task of organ segmentation. We show that subject selection via RCA canreduce the burden of annotation of new data for the target domain.
AU  - Valindria,VV
AU  - Lavdas,I
AU  - Bai,W
AU  - Kamnitsas,K
AU  - Aboagye,EO
AU  - Rockall,AG
AU  - Rueckert,D
AU  - Glocker,B
PY  - 2018///
TI  - Domain adaptation for MRI organ segmentation using reverse classification accuracy
UR  - http://arxiv.org/abs/1806.00363v1
UR  - http://hdl.handle.net/10044/1/60742
ER  -
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