Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation

Dwork, C., et al.: Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference. pp. 214–226 (2012)

2012

Earlier work this paper cites.

Kamiran, F., Calders, T.: Data preprocessing techniques for classification without discrimination. Knowledge and Information Systems 33

2012

Earlier work this paper cites.

Kamiran, F., Karim, A., Zhang, X.: Decision theory for discrimination-aware classification. In: 2012 IEEE 12th International Conference on Data Mining. pp. 924–929. IEEE (2012)

2012

Earlier work this paper cites.

Mody, P., et al.: Most important articles on cardiovascular disease among racial and ethnic minorities. Circulation: Cardiovascular Quality and Outcomes 5

2012

Earlier work this paper cites.

Kishi, S., et al.: Race–ethnic and sex differences in left ventricular structure and function: The coronary artery risk development in young adults (CARDIA) study. Journal of the American Heart Association 4

2015

Earlier work this paper cites.

Petersen, S.E., et al.: UK Biobank’s cardiovascular magnetic resonance protocol. Journal of cardiovascular magnetic resonance 18

2015

Earlier work this paper cites.

Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. arXiv preprint arXiv:1610.02413 (2016)

Original

2016

Earlier work this paper cites.

Huang, G., et al.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4700–4708 (2017)

2017

Earlier work this paper cites.

Pleiss, G., et al.: On fairness and calibration. arXiv preprint arXiv:1709.02012 (2017)

Original

2017

Earlier work this paper cites.

Yoneyama, K., et al.: Cardiovascular magnetic resonance in an adult human population: serial observations from the multi-ethnic study of atherosclerosis. Journal of Cardiovascular Magnetic Resonance 19

2017

Cited alongside, same era.

Bernard, O., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE transactions on medical imaging 37

2018

Cited alongside, same era.

Buolamwini, J., Gebru, T.: Gender shades: Intersectional accuracy disparities in commercial gender classification. In: Conference on fairness, accountability and transparency. pp. 77–91. PMLR (2018)

2018

Cited alongside, same era.

Das, A., Dantcheva, A., Bremond, F.: Mitigating bias in gender, age and ethnicity classification: a multi-task convolution neural network approach. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops. pp. 0–0 (2018)

2018

Cited alongside, same era.

Ngxande, M., Tapamo, J.R., Burke, M.: Bias remediation in driver drowsiness detection systems using generative adversarial networks. IEEE Access 8

2020

Later among the works it cites.

Raisi-Estabragh, Z., et al.: Variation of cardiac magnetic resonance radiomics features by age and sex in healthy participants from the UK Biobank. European Heart Journal 41

2020

Later among the works it cites.

Ruijsink, B., Puyol-Antón, et al.: Fully automated, quality-controlled cardiac analysis from CMR: validation and large-scale application to characterize cardiac function. Cardiovascular Imaging 13

2020

Later among the works it cites.

Wang, M., Deng, W.: Mitigating bias in face recognition using skewness-aware reinforcement learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9322–9331 (2020)

2020

Later among the works it cites.