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Deep Learning for Medical Image Analysis
TLDR
This report describes my research activities in the Hasso Plattner Institute and summarizes my Ph.D. plan and several novels, end-to-end trainable approaches for analyzing medical images using deep learning algorithm. Expand
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A Conditional Adversarial Network for Semantic Segmentation of Brain Tumor
TLDR
We propose an automatic end-to-end trainable architecture for heterogeneous brain tumor segmentation through adversarial training for the BraTS-2017 challenge. Expand
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Modelling Evapotranspiration to Increase the Accuracy of the Estimations Based on the Climatic Parameters
The potential evapotranspiration was estimated using different mass transfer-based models and was compared with the Food and Agriculture Organization Penman–Monteith model. The results showed thatExpand
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Whole Heart and Great Vessel Segmentation with Context-aware of Generative Adversarial Networks
TLDR
Inspired by vanilla generative adversarial networks, we propose a cascade of conditional GANs for semantic segmentation of cardiac magnetic resonance imaging. Expand
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Deep Neural Network with l2-Norm Unit for Brain Lesions Detection
TLDR
In this paper, we propose a novel and end-to-end trainable approach for brain lesions classification and detection by using deep Convolutional Neural Network (CNN). Expand
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Conditional Generative Refinement Adversarial Networks for Unbalanced Medical Image Semantic Segmentation
TLDR
We propose a new generative adversarial architecture to mitigate imbalance data problem in medical image semantic segmentation where the majority of pixels belongs to a healthy region and few belong to lesion or non-health region. Expand
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Brain Abnormality Detection by Deep Convolutional Neural Network
TLDR
In this paper, we describe our method for classification of brain magnetic resonance (MR) images into different abnormalities and healthy classes based on the deep neural network. Expand
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Texture classification approach based on combination of random threshold vector technique and co-occurrence matrixes
TLDR
In this paper an approach is proposed based on combination of RTV and Co-occurrence matrixes which provides high accuracy rate in texture classification. Expand
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Recurrent generative adversarial network for learning imbalanced medical image semantic segmentation
TLDR
We propose a new recurrent generative adversarial architecture named RNN-GAN to mitigate imbalance data problem in medical image semantic segmentation where the number of pixels belongs to the desired object are significantly lower than those belonging to the background. Expand
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Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data
TLDR
We propose a new generative adversarial architecture to mitigate imbalance data problem for the task of medical image semantic segmentation where the majority of pixels belong to healthy region and few belong to lesion or non-health region. Expand
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