In this work, we propose a method called MSGDD-cGAN, which first stabilizes the performance of the cGANs using multi-connections gradients flow.
Hence, infected patients can get better service from the healthcare system and decrease the number of deaths caused by the coronavirus.
One of the problems of conventional visual quality evaluation criteria such as PSNR and MSE is the lack of appropriate standards based on the human visual system (HVS).
Brain tumors count for one out of every four cancer deaths.
We propose a bifurcated 2-D model for two types of segmentation.
Knowledge distillation addresses some of the shortcomings associated with transfer learning by generalizing a complex model to a lighter model.
Reduction of the number of channels could reduce the complexity of brain-computer-interface devices.
In this paper, a new method for CNN processing in the FFT domain is proposed, which is based on input splitting.
Glioma is a common type of brain tumor, and accurate detection of it plays a vital role in the diagnosis and treatment process.
Knowledge distillation is recently proposed to transfer the knowledge of a model to another one and can be useful to cover the shortcomings of transfer learning.
For convolutional neural networks (CNNs) that have a large volume of input data, memory management becomes a major concern.
In the proposed attenuation approach, weak filters are not abruptly removed, and there is a chance for these filters to return to the network.
Intracranial tumors are groups of cells that usually grow uncontrollably.
In this paper pruning methods are investigated, and a general model which is contained the majority of pruning techniques is proposed.
The first step of this method is to determine how many scales we need to use, which depends on the width of the lines in the map of the missing region.
Digital image watermarking has been widely used in different applications such as copyright protection of digital media, such as audio, image, and video files.
There are many research works on the designing of architectures for the deep neural networks (DNN), which are named neural architecture search (NAS) methods.
By utilizing the proposed model, different methods in KD are better investigated and explored.
In each method, by employing different techniques, new solutions have been presented for increasing efficiency, accuracy, and reducing the costs.
Saliency detection is one of the most challenging problems in the fields of image analysis and computer vision.
If the pixel is located on an edge, then we use the predicted value in that direction.
One of the routine examinations that are used for prenatal care in many countries is ultrasound imaging.
In this research work, we have used color space conversion and frequency transform to achieve high robustness and transparency.
In this paper, we introduce the proposed methods in both saliency detection and retargeting.
Ultrasound imaging is a standard examination during pregnancy that can be used for measuring specific biometric parameters towards prenatal diagnosis and estimating gestational age.
Histopathology images contain essential information for medical diagnosis and prognosis of cancerous disease.
Due to the rapid growth of machine learning tools and specifically deep networks in various computer vision and image processing areas, application of Convolutional Neural Networks for watermarking have recently emerged.
In this paper, we utilize an instance segmentation neural network to obtain a class mask for separately filtering the background and foreground of an image.
The effect of using separate networks for segmentation of MR images is evaluated by comparing the results with a single network.
In recent years portable medical imaging devices such as capsule endoscopy and digital dermatoscope have been introduced and made the diagnosis procedure easier and more efficient.
Both CNN and MLP structures are simplified to reduce the number of computational operations.
Deep neural networks have shown great achievements in solving complex problems.
In the detection phase, we employ both HSV and RGB color space information for segmentation of specular reflections.
In this paper we propose an automated method for segmenting the left ventricle in cardiac MR images.
Wireless capsule endoscopy (WCE) is an effective means of diagnosis of gastrointestinal disorders.
By increasing the volume of telemedicine information, the need for medical image compression has become more important.
In this paper, we propose an adaptive 3D region growing with subject-specific conditions.
In this study a reversible watermarking is investigated with focusing on increasing the embedding capacity and reducing the distortion in medical images.
Recent advances in capsule endoscopy systems have introduced new methods and capabilities.
The need for CT scan analysis is growing for pre-diagnosis and therapy of abdominal organs.
In this paper a saliency map is proposed, based on image context detection using semantic segmentation as a high level feature.
One of the essential tasks in medical image analysis is segmentation and accurate detection of borders.
The proposed method is devoid of complex and iterative structure to save power and reduce the response time.
In this framework we use our catheter detection and tracking method which detects the catheter by finding its ridge in the first frame and traces in other frames by fitting a second order polynomial on it.
In this paper a low complexity de-noising method is proposed that removes the noise by local analysis of the image blocks.
Then, a stacked neural network model is proposed, which learns the extracted features and accurately evaluates the quality of stereo images.
In this paper we introduce a system of hand gesture recognition based on a deep learning approach.
The input image is preprocessed and then its patches are fed to a convolutional neural network (CNN).