@InProceedings{Deep_Adversarial_Networks,
author="Zhang, Yizhe
and Yang, Lin
and Chen, Jianxu
and Fredericksen, Maridel
and Hughes, David P.
and Chen, Danny Z.",
editor="Descoteaux, Maxime
and Maier-Hein, Lena
and Franz, Alfred
and Jannin, Pierre
and Collins, D. Louis
and Duchesne, Simon",
title="Deep Adversarial Networks for Biomedical Image Segmentation Utilizing Unannotated Images",
booktitle="Medical Image Computing and Computer Assisted Intervention − MICCAI 2017",
year="2017",
publisher="Springer International Publishing",
address="Cham",
pages="408--416",
abstract="Semantic segmentation is a fundamental problem in biomedical image analysis. In biomedical practice, it is often the case that only limited annotated data are available for model training. Unannotated images, on the other hand, are easier to acquire. How to utilize unannotated images for training effective segmentation models is an important issue. In this paper, we propose a new deep adversarial network (DAN) model for biomedical image segmentation, aiming to attain consistently good segmentation results on both annotated and unannotated images. Our model consists of two networks: (1) a segmentation network (SN) to conduct segmentation; (2) an evaluation network (EN) to assess segmentation quality. During training, EN is encouraged to distinguish between segmentation results of unannotated images and annotated ones (by giving them different scores), while SN is encouraged to produce segmentation results of unannotated images such that EN cannot distinguish these from the annotated ones. Through an iterative adversarial training process, because EN is constantly ``criticizing'' the segmentation results of unannotated images, SN can be trained to produce more and more accurate segmentation for unannotated and unseen samples. Experiments show that our proposed DAN model is effective in utilizing unannotated image data to obtain considerably better segmentation.",
isbn="978-3-319-66179-7"
}

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}

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	pages = {102035},
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}

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@article{LossOdyssey,
title = {Loss Odyssey in Medical Image Segmentation},
journal = {Medical Image Analysis},
volume = {71},
pages = {102035},
year = {2021},
author = {Jun Ma and Jianan Chen and Matthew Ng and Rui Huang and Yu Li and Chen Li and Xiaoping Yang and Anne L. Martel}
}

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}