UNeXt: MLP-Based Rapid Medical Image Segmentation Network

Valanarasu, Jeya Maria Jose; Patel, Vishal M.

doi:10.1007/978-3-031-16443-9_3

Cited by 291 publications

(155 citation statements)

References 18 publications

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“…This section presents the results of the experiments conducted on the proposed ConvSegNet model and the existing methods. For a fair comparison, we considered six standard state-of-the-art deep learning architectures as benchmark models in U-Net [28], ResU-Net [30], U-Net++ [29], HardDNet-MSEG [50], FANet [42] and UNeXt [43].…”

Section: Methodsmentioning

confidence: 99%

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ConvSegNet: Automated Polyp Segmentation From Colonoscopy Using Context Feature Refinement With Multiple Convolutional Kernel Sizes

et al. 2023

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Colorectal cancer occurs in the rectal of humans, and early detection has been proved to reduce its mortality rate. Colonoscopy is the standard used in detecting the presence of polyps in the rectal, and accurate segmentation of the polyps from colonoscopy images often provides helpful information for early diagnosis and treatment. Although existing deep learning models often achieve high segmentation performance when tested on the same dataset used in model training; still, their performance often degrades when applied to out-of-distribution datasets, leading to low model generalization or overfitting. This challenge is often associated with the quality of the features learnt from the input images. In this work, a novel Context Feature Refinement (CFR) module is proposed to address the challenge of low model generalization and segmentation performance. The CFR module is built to extract contextual information from the incoming feature map by using multiple parallel convolutional layers with progressively increasing kernel sizes. Using multiple parallel convolutions with different kernel sizes helped to extract more efficient multi-scale contextual information and thus enabled the network to effectively identify and segment small and fine details, as well as larger and more complex structures in the input images. Extensive experiments on three public benchmark datasets in CVC-ClinicDB, Kvasir-SEG, and BKAI-NeoPolyp showed that the proposed ConvSegNet model achieved jaccard, dice and F2 scores of 0.8650, 0.9177, and 0.9328 on CVC-ClinicDB, 0.7936, 0.8618, and 0.8855 on Kvasir-SEG, and 0.8045, 0.8747 and 0.8909 on BKAI-NeoPolyp datasets respectively. Also, an improved generalization performance was achieved by the ConvSegNet model, compared to the benchmark polyp segmentation models.

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Section: Methodsmentioning

confidence: 99%

“…In a bid to develop lightweight segmentation models, Valanarasu et al [43], proposed UNeXt, a convolutional multilayer perceptron based network for image segmentation. The model was designed with an early convolutional stage and a MLP in the latent stage.…”

Section: A Polyp Segmentation Architecturesmentioning

confidence: 99%

ConvSegNet: Automated Polyp Segmentation From Colonoscopy Using Context Feature Refinement With Multiple Convolutional Kernel Sizes

et al. 2023

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“…We carried out experiments on the tooth dataset with other state-of-the-art methods to show the proposed approach’s effectiveness. As shown in Table 1 , we compared our result with other state-of-the-art methods, i.e., U-Net ( Ronneberger et al, 2015) , DenseASPP ( Yang et al, 2018) , BiSeNet ( Yu et al, 2018) , PSPNet ( Zhao et al, 2017) , PAN ( Li et al, 2018), DeepLabV3 ( Chen et al, 2017) , DeepLabV3+ ( Chen et al, 2018) , and UNeXt ( Valanarasu and Patel, 2022) . These codes are available online, and we follow the authors’ instructions to train the models on the tooth CBCT dataset.…”

Section: Methodsmentioning

confidence: 99%

Clinical tooth segmentation based on local enhancement

Zhang

Yang

et al. 2022

Front. Mol. Biosci.

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The tooth arrangements of human beings are challenging to accurately observe when relying on dentists’ naked eyes, especially for dental caries in children, which is difficult to detect. Cone-beam computer tomography (CBCT) is used as an auxiliary method to measure patients’ teeth, including children. However, subjective and irreproducible manual measurements are required during this process, which wastes much time and energy for the dentists. Therefore, a fast and accurate tooth segmentation algorithm that can replace repeated calculations and annotations in manual segmentation has tremendous clinical significance. This study proposes a local contextual enhancement model for clinical dental CBCT images. The local enhancement model, which is more suitable for dental CBCT images, is proposed based on the analysis of the existing contextual models. Then, the local enhancement model is fused into an encoder–decoder framework for dental CBCT images. At last, extensive experiments are conducted to validate our method.

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“…Another part of the DSLA is Axial-mlp 21 , which enhances the global modeling capabilities of the network with less computation. We achieve interaction between different layers, enriching the semantic information and reducing the conflict of fusing different features when compared to UNeXt 22 . Further advancements in global modeling capabilities allow for even better network segmentation performance.…”

Section: Introductionmentioning

confidence: 99%

Dynamic hierarchical multi-scale fusion network with axial MLP for medical image segmentation

Cheng

Wang

2023

Sci Rep

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Medical image segmentation provides various effective methods for accuracy and robustness of organ segmentation, lesion detection, and classification. Medical images have fixed structures, simple semantics, and diverse details, and thus fusing rich multi-scale features can augment segmentation accuracy. Given that the density of diseased tissue may be comparable to that of surrounding normal tissue, both global and local information are critical for segmentation results. Therefore, considering the importance of multi-scale, global, and local information, in this paper, we propose the dynamic hierarchical multi-scale fusion network with axial mlp (multilayer perceptron) (DHMF-MLP), which integrates the proposed hierarchical multi-scale fusion (HMSF) module. Specifically, HMSF not only reduces the loss of detail information by integrating the features of each stage of the encoder, but also has different receptive fields, thereby improving the segmentation results for small lesions and multi-lesion regions. In HMSF, we not only propose the adaptive attention mechanism (ASAM) to adaptively adjust the semantic conflicts arising during the fusion process but also introduce Axial-mlp to improve the global modeling capability of the network. Extensive experiments on public datasets confirm the excellent performance of our proposed DHMF-MLP. In particular, on the BUSI, ISIC 2018, and GlaS datasets, IoU reaches 70.65%, 83.46%, and 87.04%, respectively.

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UNeXt: MLP-Based Rapid Medical Image Segmentation Network

Cited by 291 publications

References 18 publications

ConvSegNet: Automated Polyp Segmentation From Colonoscopy Using Context Feature Refinement With Multiple Convolutional Kernel Sizes

ConvSegNet: Automated Polyp Segmentation From Colonoscopy Using Context Feature Refinement With Multiple Convolutional Kernel Sizes

Clinical tooth segmentation based on local enhancement

Dynamic hierarchical multi-scale fusion network with axial MLP for medical image segmentation

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