About: MIRD-Net for Medical Image Segmentation

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	MIRD-Net for Medical Image Segmentation
Creator	Huang, Yongfeng Dai, Hao Li, Xueyang Liu, Lihao Yan, Cairong
Source	PMC
abstract	Medical image segmentation is a fundamental and challenging problem for analyzing medical images due to the approximate pixel values of adjacent tissues in boundary and the non-linear feature between pixels. Although fully convolutional neural networks such as U-Net has demonstrated impressive performance on medical image segmentation, distinguishing subtle features between different categories after pooling layers is still a difficult task, which affects the segmentation accuracy. In this paper, we propose a Mini-Inception-Residual-Dense (MIRD) network named MIRD-Net to deal with this problem. The key point of our proposed MIRD-Net is MIRD Block. It takes advantage of Inception, Residual Block (RB) and Dense Block (DB), aiming to make the network obtain more features to help improve the segmentation accuracy. There is no pooling layer in MIRD-Net. Such a design avoids loss of information during forward propagation. Experimental results show that our framework significantly outperforms U-Net in six different image segmentation tasks and its parameters are only about 1/50 of U-Net.
has issue date	2020-04-17(xsd:dateTime)
bibo:doi	10.1007/978-3-030-47436-2_16
has license	no-cc
sha1sum (hex)	12112efc0b97a588ae6740e952f26807a2a8280e
schema:url	https://doi.org/10.1007/978-3-030-47436-2_16
resource representing a document's title	MIRD-Net for Medical Image Segmentation
has PubMed Central identifier	PMC7206284
schema:publication	Advances in Knowledge Discovery and Data Mining
resource representing a document's body	covid:12112efc0b97a588ae6740e952f26807a2a8280e#body_text
is schema:about of	named entity 'problem' named entity 'network' named entity 'loss' named entity 'pixel' named entity 'Inception' named entity 'image segmentation' named entity 'pixel' named entity 'non-linear' named entity 'convolutional layers' named entity 'U-Net' named entity 'U-Net' named entity 'downsampling' named entity 'pixel' named entity 'MRI' named entity 'loss function' named entity 'GPU' named entity 'non-linear' named entity 'cross-validation' named entity 'convolutional networks' named entity 'convolution layer' named entity 'ReLU' named entity 'neural network' named entity 'green circles' named entity 'pixel' named entity 'cross validation' named entity 'upsampling' named entity 'Conditional Random Field' named entity 'ground truth' named entity 'downsampling' named entity '19, 20' named entity 'Computer's graphics' named entity 'U-Net' named entity 'loss function' named entity 'parameter space' named entity 'high resolution' named entity 'ReLU' named entity 'U-Net' named entity 'image segmentation' named entity 'image segmentation' named entity 'convolutional neural networks' named entity 'image segmentation' named entity 'CPU' named entity 'clinical diagnosis' named entity 'U-Net' named entity 'Dice coefficient' named entity 'U-Net' named entity 'downsampling' named entity 'U-Net' named entity 'Majority voting' named entity 'downsampling' named entity '1 × 1' named entity 'pooling layer' named entity 'U-Net' named entity 'RBs' named entity 'U-Net' named entity 'RBs' named entity 'U-Net' named entity 'standard deviation' named entity 'pixel' named entity 'Dice coefficient' named entity 'brain MRI' named entity 'RBs' named entity 'network architecture' named entity 'U-Net' named entity 'Experimental results' named entity 'image segmentation' named entity 'pooling layer' named entity 'Residual' named entity 'Windows 10' named entity 'Dice coefficient' named entity 'pixels' named entity 'DEAL' named entity 'DIFFICULT' named entity 'PIXEL' named entity 'MEDICAL' named entity 'MEDICAL IMAGE SEGMENTATION' named entity 'MIRD' named entity 'NON-LINEAR' named entity 'U-Net'

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