2020
DOI: 10.1016/j.neucom.2019.01.110
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AdaResU-Net: Multiobjective adaptive convolutional neural network for medical image segmentation

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Cited by 111 publications
(41 citation statements)
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“…Baumgartner et al [6] used 2D U-Net to segment the CMR data, they processed the 3D data in a slice-by-slice fashion, and there is no correlation between each 2D slice, as the result this method lose the spatial structure information in the original data. In general, 2D medical image segmentation methods [7], [8] have the problem of losing spatial information.…”
Section: A 2d Convolution On Planesmentioning
confidence: 99%
“…Baumgartner et al [6] used 2D U-Net to segment the CMR data, they processed the 3D data in a slice-by-slice fashion, and there is no correlation between each 2D slice, as the result this method lose the spatial structure information in the original data. In general, 2D medical image segmentation methods [7], [8] have the problem of losing spatial information.…”
Section: A 2d Convolution On Planesmentioning
confidence: 99%
“…The comparison of the obtained results with previous works is also reported in Table 8. In this section, we compare the obtained results versus U-Net [32], Dense U-Net [36], Res U-Net [35], and Non-Bypass Dense [37] which are all manually designed networks; also, AdaResU-Net [30] and EvoU-Net [31] that are evolutionary networks and finally with NAS U-Net [38] which was developed using reinforcement learning. As shown in Table 8, in all six datasets, our proposed DenseRes evolutionary models obtained the best accuracy for image segmentation.…”
Section: F Comparison With Prior Workmentioning
confidence: 99%
“…Since the variable-length encoding strategy is employed for network representation, new crossover and mutation operations are also proposed. AdaResU-Net [30] is another evolutionary model, where a fixed network structure is utilised. However, a number of parameters, including learning rate, dropout probability, the number of filters, activation function, and the filter size of each convolution layer, are specified using a multi-objective evolutionary algorithm.…”
Section: Introductionmentioning
confidence: 99%
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“…Feng et al [4] segmented retinal vessels by a cross-connected convolutional network and multi-scale features. Baldeon-Calisto et al [5] segmented medical images by a multiobjective adaptive convolutional neural network. Li et al [6] proposed a 3D fully convolutional network to rationally fuse the complementary information in PET/CT for accurate tumor segmentation.…”
Section: Introductionmentioning
confidence: 99%