Hyper Vision Net: Kidney Tumor Segmentation Using Coordinate Convolutional Layer and Attention Unit

Nathan, Sabari; Beham, M. Parisa; Roomi, S. M. Md. Mansoor

doi:10.1007/978-981-15-8697-2_57

Cited by 11 publications

(4 citation statements)

References 18 publications

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“…Sabarinathan et al . [ 26 ] proposed that the loss function should be the sums of the categorical cross-entropy dice loss channel one (C0) and dice loss channel two (C1), as defined in Eq. (1).…”

Section: Proposed Methodsmentioning

confidence: 99%

Weight Pruning-UNet

Rao

Chatterjee

Sharma

2022

Journal of Medical Signals &Amp; Sensors

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Background: Accurate semantic segmentation of kidney tumors in computed tomography (CT) images is difficult because tumors feature varied forms and occasionally, look alike. The KiTs19 challenge sets the groundwork for future advances in kidney tumor segmentation. Methods: We present weight pruning (WP)-UNet, a deep network model that is lightweight with a small scale; it involves few parameters with a quick assumption time and a low floating-point computational complexity. Results: We trained and evaluated the model with CT images from 210 patients. The findings implied the dominance of our method on the training Dice score (0.98) for the kidney tumor region. The proposed model only uses 1,297,441 parameters and 7.2e floating-point operations, three times lower than those for other network models. Conclusions: The results confirm that the proposed architecture is smaller than that of UNet, involves less computational complexity, and yields good accuracy, indicating its potential applicability in kidney tumor imaging.

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Section: Proposed Methodsmentioning

confidence: 99%

Weight Pruning-UNet

Rao

Chatterjee

Sharma

2022

Journal of Medical Signals &Amp; Sensors

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“…Adam makes an average in the rst and second moments of gradients to adapt the learning rate parameter. Sabarinathan et al (2019) proposed that the loss function be the sum of the categorical cross-entropy Dice loss channel one (C0) and Dice loss channel two (C1), as de ned in Eq. (1).…”

Section: Loss Functionmentioning

confidence: 99%

WP-UNet: Weight Pruning U-Net with Depth-wise Separable Convolutions for Semantic Segmentation of Kidney Tumours

Rao¹,

Chatterjee²,

Sha

2021

Preprint

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Background: Accurate semantic segmentation of kidney tumours in computed tomography (CT) images is difficult because tumours feature varied forms and, occasionally, look alike. The KiTs19 challenge sets the groundwork for future advances in kidney tumour segmentation.Methods: We present WP-UNet, a deep network model that is lightweight with a small scale; it involves few parameters with a quick assumption time and a low floating-point computational complexity.Results: We trained and evaluated the model with CT images from 300 patients. The findings implied the dominance of our method on the training Dice score (0.98) for the kidney tumour region. The proposed model only uses 1,297,441 parameters and 7.2e FLOPS, three times lower than those for other network models. Conclusions: The results confirm that the proposed architecture is smaller than that of U-Net, involves less computational complexity, and yields good accuracy, indicating its potential applicability in kidney tumour imaging.

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“…Output features of the encoder are fed to the convolution block attention module [51] (CBAM) and in the skip connection upsampled features concatenated with encoder block and the output of the CBAM block. Inspired by the Hyper Vision Net [39] model, in this work the three hyper vision layers in the decoder part were introduced. The output of these hyper vision layers are fused and supervised to obtain the enhanced image.…”

Section: Image Lab Teammentioning

confidence: 99%

“…The convolutional layer and the residual dense attention blocks are utilized for better performance. The proposed network has the properties of an encoder and decoder structure of a vanilla U-Net [39,38]. During down-sampling three blocks have been used in the encoder phase.…”

Section: Image Lab Teammentioning

confidence: 99%

NTIRE 2020 Challenge on Image Demoireing: Methods and Results

Yuan

Timofte²,

Leonardis³

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020. Demoireing is a difcult task of removing moire patterns from an image to reveal an underlying clean image. The challenge was divided into two tracks. Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image. Track 2 focused on the burst demoireing problem, where a set of degraded moire images of the same scene were provided as input, with the goal of producing a single demoired image as output. The methods were ranked in terms of their fidelity, measured using the peak signal-to-noise ratio (PSNR) between the ground truth clean images and the restored images produced by the participants' methods. The tracks had 142 and 99 registered participants, respectively, with a total of 14 and 6 submissions in the final testing stage. The entries span the current state-of-the-art in image and burst image demoireing problems.

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Hyper Vision Net: Kidney Tumor Segmentation Using Coordinate Convolutional Layer and Attention Unit

Cited by 11 publications

References 18 publications

Weight Pruning-UNet

Weight Pruning-UNet

WP-UNet: Weight Pruning U-Net with Depth-wise Separable Convolutions for Semantic Segmentation of Kidney Tumours

NTIRE 2020 Challenge on Image Demoireing: Methods and Results

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