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arXiv: Image and Video Processing
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Papers 1917
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#1Dmitrii Lachinov (Medical University of Vienna)
#2Elena Shipunova (Intel)
Last.Vadim Turlapov (N. I. Lobachevsky State University of Nizhny Novgorod)H-Index: 3
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The segmentation of brain tumors in multimodal MRIs is one of the most challenging tasks in medical image analysis. The recent state of the art algorithms solving this task is based on machine learning approaches and deep learning in particular. The amount of data used for training such models and its variability is a keystone for building an algorithm with high representation power. In this paper, we study the relationship between the performance of the model and the amount of data employed dur...
#1Andreas Kofler (Charité)H-Index: 1
#2Marc Dewey (Charité)H-Index: 40
Last.Markus Haltmeier (University of Innsbruck)H-Index: 26
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In this work, we propose an iterative reconstruction scheme (ALONE - Adaptive Learning Of NEtworks) for 2D radial cine MRI based on ground truth-free unsupervised learning of shallow convolutional neural networks. The network is trained to approximate patches of the current estimate of the solution during the reconstruction. By imposing a shallow network topology and constraining the L_2norm of the learned filters, the network's representation power is limited in order not to be able to recov...
#1Xi Zhang (SJTU: Shanghai Jiao Tong University)
#2Xiaolin Wu (McMaster University)
In many professional fields, such as medicine, remote sensing and sciences, users often demand image compression methods to be mathematically lossless. But lossless image coding has a rather low compression ratio (around 2:1 for natural images). The only known technique to achieve significant compression while meeting the stringent fidelity requirements is the methodology of \ell_\inftyconstrained coding that was developed and standardized in nineties. We make a major progress in $\ell_\infty...
#1Yueyu HuH-Index: 5
#2Wenhan YangH-Index: 12
Last.Jiaying LiuH-Index: 22
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Image compression is one of the most fundamental techniques and commonly used applications in the image and video processing field. Earlier methods built a well-designed pipeline, and efforts were made to improve all modules of the pipeline by handcrafted tuning. Later, tremendous contributions were made, especially when data-driven methods revitalized the domain with their excellent modeling capacities and flexibility in incorporating newly designed modules and constraints. Despite great progre...
#1Roger D. Soberanis-Mukul (TUM: Technische Universität München)
#2Nassir NavabH-Index: 61
Last.Shadi AlbarqouniH-Index: 10
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Organ segmentation is an important pre-processing step in many computer assisted intervention and diagnosis methods. In recent years, CNNs have dominated the state of the art in this task. Organ segmentation scenarios present a challenging environment for these methods due to high variability in shape and similarity with background. This leads to the generation of false negative and false positive regions in the output segmentation. In this context, the uncertainty analysis of the model can prov...
#1Ruiguo ZhuH-Index: 2
#2Hong YuH-Index: 5
Last.Jian WangH-Index: 1
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Ghost imaging incorporating deep learning technology has recently attracted much attention in the optical imaging field. However, deterministic illumination and multiple exposure are still essential in most scenarios. Here we propose a ghost imaging scheme based on a novel conjugate-decoding deep learning framework (Y-net), which works well under both deterministic and indeterministic illumination. Benefited from the end-to-end characteristic of our network, the image of a sample can be achieved...
#2Ashok HandaH-Index: 12
Last.Regent Lee (University of Oxford)H-Index: 15
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Existing methods to reconstruct vascular structures from a computed tomography (CT) angiogram rely on injection of intravenous contrast to enhance the radio-density within the vessel lumen. However, pathological changes can be present in the blood lumen, vessel wall or a combination of both that prevent accurate reconstruction. In the example of aortic aneurysmal disease, a blood clot or thrombus adherent to the aortic wall within the expanding aneurysmal sac is present in 70-80% of cases. These...
#1Hamid Reza Boveiri (SUTECH: Shiraz University of Technology)H-Index: 5
#2Raouf KhayamiH-Index: 6
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Image-guided interventions are saving the lives of a large number of patients where the image registration problem should indeed be considered as the most complex and complicated issue to be tackled. On the other hand, the recently huge progress in the field of machine learning made by the possibility of implementing deep neural networks on the contemporary many-core GPUs opened up a promising window to challenge with many medical applications, where the registration is not an exception. In this...
#1Dan Malowany (BGU: Ben-Gurion University of the Negev)H-Index: 1
#2Hugo Guterman (BGU: Ben-Gurion University of the Negev)H-Index: 24
Findings in recent years on the sensitivity of convolutional neural networks to additive noise, light conditions and to the wholeness of the training dataset, indicate that this technology still lacks the robustness needed for the autonomous robotic industry. In an attempt to bring computer vision algorithms closer to the capabilities of a human operator, the mechanisms of the human visual system was analyzed in this work. Recent studies show that the mechanisms behind the recognition process in...
#1Sanjeev Kumar (IIT-KGP: Indian Institute of Technology Kharagpur)H-Index: 5
Last.Pranab K. DuttaH-Index: 18
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Lensless in-line holography is a simple, portable, and cost-effective method of imaging especially for the biomedical microscopy applications. We propose a multiplicative gradient descent optimization based method to obtain multi-depth imaging from a single hologram acquired in this imaging system. We further extend the method to achieve phase imaging from a single hologram. Negative-log-likelihood functional with the assumption of poisson noise has been used as the cost function to be minimized...
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Top fields of study
Deep learning
Pattern recognition
Computer vision
Computer science
Convolutional neural network
Segmentation