TY - GEN
T1 - Learning fixed points in generative adversarial networks
T2 - 17th IEEE/CVF International Conference on Computer Vision, ICCV 2019
AU - Siddiquee, Md Mahfuzur Rahman
AU - Zhou, Zongwei
AU - Tajbakhsh, Nima
AU - Feng, Ruibin
AU - Gotway, Michael
AU - Bengio, Yoshua
AU - Liang, Jianming
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Generative adversarial networks (GANs) have ushered in a revolution in image-to-image translation. The development and proliferation of GANs raises an interesting question: Can we train a GAN to remove an object, if present, from an image while otherwise preserving the image? Specifically, can a GAN ''virtually heal'' anyone by turning his medical image, with an unknown health status (diseased or healthy), into a healthy one, so that diseased regions could be revealed by subtracting those two images? Such a task requires a GAN to identify a minimal subset of target pixels for domain translation, an ability that we call fixed-point translation, which no GAN is equipped with yet. Therefore, we propose a new GAN, called Fixed-Point GAN, trained by (1) supervising same-domain translation through a conditional identity loss, and (2) regularizing cross-domain translation through revised adversarial, domain classification, and cycle consistency loss. Based on fixed-point translation, we further derive a novel framework for disease detection and localization using only image-level annotation. Qualitative and quantitative evaluations demonstrate that the proposed method outperforms the state of the art in multi-domain image-to-image translation and that it surpasses predominant weakly-supervised localization methods in both disease detection and localization. Implementation is available at https://github.com/jlianglab/Fixed-Point-GAN.
AB - Generative adversarial networks (GANs) have ushered in a revolution in image-to-image translation. The development and proliferation of GANs raises an interesting question: Can we train a GAN to remove an object, if present, from an image while otherwise preserving the image? Specifically, can a GAN ''virtually heal'' anyone by turning his medical image, with an unknown health status (diseased or healthy), into a healthy one, so that diseased regions could be revealed by subtracting those two images? Such a task requires a GAN to identify a minimal subset of target pixels for domain translation, an ability that we call fixed-point translation, which no GAN is equipped with yet. Therefore, we propose a new GAN, called Fixed-Point GAN, trained by (1) supervising same-domain translation through a conditional identity loss, and (2) regularizing cross-domain translation through revised adversarial, domain classification, and cycle consistency loss. Based on fixed-point translation, we further derive a novel framework for disease detection and localization using only image-level annotation. Qualitative and quantitative evaluations demonstrate that the proposed method outperforms the state of the art in multi-domain image-to-image translation and that it surpasses predominant weakly-supervised localization methods in both disease detection and localization. Implementation is available at https://github.com/jlianglab/Fixed-Point-GAN.
UR - http://www.scopus.com/inward/record.url?scp=85081914095&partnerID=8YFLogxK
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U2 - 10.1109/ICCV.2019.00028
DO - 10.1109/ICCV.2019.00028
M3 - Conference contribution
AN - SCOPUS:85081914095
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 191
EP - 200
BT - Proceedings - 2019 International Conference on Computer Vision, ICCV 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 October 2019 through 2 November 2019
ER -