A Conditional Adversarial Network for Semantic Segmentation of Brain Tumor

@inproceedings{Rezaei2017ACA,
  title={A Conditional Adversarial Network for Semantic Segmentation of Brain Tumor},
  author={Mina Rezaei and Konstantin Harmuth and Willi Gierke and Thomas Kellermeier and Martin Fischer and Haojin Yang and Christoph Meinel},
  booktitle={BrainLes@MICCAI},
  year={2017}
}
Automated brain lesion detection is an important and very challenging clinical diagnostic task, due to the lesions’different sizes, shapes, contrasts, and locations. [] Key Method Inspired by classical generative adversarial network, the proposed network has two components: the “Discriminator” and the “Generator”. We use a patient-wise fully convolutional neural networks (FCNs) as the segmentor network to generate segmentation label maps.
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References

SHOWING 1-10 OF 25 REFERENCES
Adversarial Deep Structural Networks for Mammographic Mass Segmentation
TLDR
An end-to-end network for mammographic mass segmentation using a fully convolutional network to model potential function, followed by a CRF to perform structural learning and adversarial training to control over-fitting.
U-Net: Convolutional Networks for Biomedical Image Segmentation
TLDR
It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
TLDR
This work introduces a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrates that they are a strong candidate for unsupervised learning.
Improved Techniques for Training GANs
TLDR
This work focuses on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic, and presents ImageNet samples with unprecedented resolution and shows that the methods enable the model to learn recognizable features of ImageNet classes.
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
In this paper we present a method for learning a discriminative classifier from unlabeled or partially labeled data. Our approach is based on an objective function that trades-off mutual information
Deep multi-scale video prediction beyond mean square error
TLDR
This work trains a convolutional network to generate future frames given an input sequence and proposes three different and complementary feature learning strategies: a multi-scale architecture, an adversarial training method, and an image gradient difference loss function.
A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
TLDR
This paper proposes to learn an adversarial network that generates examples with occlusions and deformations, the goal of the adversary is to generate examples that are difficult for the object detector to classify and both the original detector and adversary are learned in a joint manner.
Perceptual Generative Adversarial Networks for Small Object Detection
TLDR
This work addresses the small object detection problem by developing a single architecture that internally lifts representations of small objects to super-resolved ones, achieving similar characteristics as large objects and thus more discriminative for detection.
Conditional Generative Adversarial Nets
TLDR
The conditional version of generative adversarial nets is introduced, which can be constructed by simply feeding the data, y, to the generator and discriminator, and it is shown that this model can generate MNIST digits conditioned on class labels.
Learning What and Where to Draw
TLDR
This work proposes a new model, the Generative Adversarial What-Where Network (GAWWN), that synthesizes images given instructions describing what content to draw in which location, and shows high-quality 128 x 128 image synthesis on the Caltech-UCSD Birds dataset.
...
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