Resampling and super-resolution of hexagonally sampled images using deep learning

Flaute, Dylan; Hardie, Russell C.; Elwarfalli, Hamed

doi:10.1117/1.oe.60.10.103105

Cited by 1 publication

(1 citation statement)

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“…In [32], residual units and astrous convolution were regarded as the basic convolution unit and heightened the learning ability of the model on features. Flaute et al [33] proposed a residual channel attention network for sampling and recovery of super-resolution images, which can well preserve the integrity of features learned from the encoder. Punn et al [34] designed a residual space cross-attention-guided inception-Unet model that fused shallow…”

Section: Residual and Pyramid Attention Networkmentioning

confidence: 99%

Dermoscopic image segmentation based on Pyramid Residual Attention Module

Jiang¹,

Cheng²,

Dong³

et al. 2022

PLoS ONE

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We propose a stacked convolutional neural network incorporating a novel and efficient pyramid residual attention (PRA) module for the task of automatic segmentation of dermoscopic images. Precise segmentation is a significant and challenging step for computer-aided diagnosis technology in skin lesion diagnosis and treatment. The proposed PRA has the following characteristics: First, we concentrate on three widely used modules in the PRA. The purpose of the pyramid structure is to extract the feature information of the lesion area at different scales, the residual means is aimed to ensure the efficiency of model training, and the attention mechanism is used to screen effective features maps. Thanks to the PRA, our network can still obtain precise boundary information that distinguishes healthy skin from diseased areas for the blurred lesion areas. Secondly, the proposed PRA can increase the segmentation ability of a single module for lesion regions through efficient stacking. The third, we incorporate the idea of encoder-decoder into the architecture of the overall network. Compared with the traditional networks, we divide the segmentation procedure into three levels and construct the pyramid residual attention network (PRAN). The shallow layer mainly processes spatial information, the middle layer refines both spatial and semantic information, and the deep layer intensively learns semantic information. The basic module of PRAN is PRA, which is enough to ensure the efficiency of the three-layer architecture network. We extensively evaluate our method on ISIC2017 and ISIC2018 datasets. The experimental results demonstrate that PRAN can obtain better segmentation performance comparable to state-of-the-art deep learning models under the same experiment environment conditions.

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Section: Residual and Pyramid Attention Networkmentioning

confidence: 99%