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 - Funding Information:
Acknowledgments: This research has been supported partially by ASU and Mayo Clinic through a Seed Grant and an Innovation Grant, and partially by NIH under Award Number R01HL128785. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH. We thank Zuwei Guo for helping us with the implementation of abaseline method.
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.
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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 -