RT‐Unet: An advanced network based on residual network and transformer for medical image segmentation

Li, Bo; Liu, Sikai; Wu, Fei; Li, GuangHui; Zhong, Meiling; Guan, Xiaohui

doi:10.1002/int.22956

Cited by 18 publications

(5 citation statements)

References 24 publications

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“…The redundancy and long-distance interdependence between pixels in network learning are successfully resolved by UniFormer [28], which uses CNN and transformer to extract global and local information. Conformer [36] and RFTNet [37] improve the feature representation capacity of network learning by fusing the capabilities of transformer to create global associations with the capabilities of CNN to extract local features. The ability of network learning to represent features.…”

Section: Convolution and Transformermentioning

confidence: 99%

Semantic segmentation feature fusion network based on transformer

Li,

Cui,

Zhang

2024

Preprint

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Convolutional neural networks have demonstrated efficacy in acquiring local features and spatial details; however, they struggle to obtain global information, which could potentially compromise the segmentation of important regions of an image. Transformer can increase the expressiveness of pixels by establishing global relationships between them. Moreover, some transformer-based self-attentive methods do not combine the advantages of convolution, which makes the model require more computational parameters. This work uses both Transformer and CNN structures to improve the relationship between image-level regions and global information to improve segmentation accuracy and performance in order to address these two issues and improve the semantic segmentation segmentation results at the same time. We first build a Feature Alignment Module (FAM) module to enhance spatial details and improve channel representations. Second, we compute the link between similar pixels using a Transformer structure, which enhances the pixel representation. Finally, we design a Pyramid Convolutional Pooling Module (PCPM) that both compresses and enriches the feature maps, as well as determines the global correlations among the pixels, to reduce the computational burden on the transformer. These three elements come together to form a transformer-based semantic segmentation feature fusion network (FFTNet). Our method yields 82.5% mIoU, according to experimental results based on the Cityscapes test dataset. Furthermore, we conducted various visualization tests using the Pascal VOC 2012 and Cityscapes datasets. The results show that our approach outperforms alternative approaches.

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Section: Convolution and Transformermentioning

confidence: 99%

Semantic segmentation feature fusion network based on transformer

Li,

Cui,

Zhang

2024

Preprint

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“…In addition, one of the main object detection paradigms is the feature pyramid network [1,26,33,34], which fuses image features of diferent scales through the top-down and bottom-up paths. Tis way results in the bottom feature that can also share the rich semantic information with the top feature [35,36]. For instance, Bi-FPN [37] enhances the representation ability of features by adding residual connections to the original structure and removing nodes without feature fusion to reduce the amount of computation.…”

Section: Muti-scale Featuresmentioning

confidence: 99%

ALFPN: Adaptive Learning Feature Pyramid Network for Small Object Detection

Chen

Wang

Ruan

et al. 2023

International Journal of Intelligent Systems

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Object detection has become a crucial technology in intelligent vision systems, enabling automatic detection of target objects. While most detectors perform well on open datasets, they often struggle with small-scale objects. This is due to the traditional top-down feature fusion methods that weaken the semantic and location information of small objects, leading to poor classification performance. To address this issue, we propose a novel feature pyramid network, the adaptive learnable feature pyramid network (ALFPN). Our approach features an adaptive feature inspection that incorporates learnable fusion coefficients in the fusion of different levels of feature layers, aiding the network in learning features with less noise. In addition, we construct a context-aligned supervisor that adjusts the feature maps fused at different levels to avoid scaling-related offset effects. Our experiments demonstrate that our method achieves state-of-the-art results and is highly robust for the small object detection on the TT-100K, PASCAL VOC, and COCO datasets. These findings indicate that a model’s ability to extract discriminant features is positively correlated with its performance in detecting small objects.

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“…In the feld of medical image segmentation based on deep learning [8][9][10], the encoderdecoder structure is one of the most commonly used network structures [11][12][13]. UNet [7] and SegNet [14] are two representatives of the encoder-decoder structure-based methods.…”

Section: Encoder-decoder Structurementioning

confidence: 99%

An Edge-Assisted Computing and Mask Attention Based Network for Lung Region Segmentation

Wang

Zhong

Huang

et al. 2023

International Journal of Intelligent Systems

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Recent years have witnessed the success of encoder-decoder structure-based approaches in lung region segmentation of chest X-ray (CXR) images. However, accurate lung region segmentation is still challenging due to the following three issues: (1) inaccurate lung region segmentation boundaries, (2) existence of lesion-related artifacts (e.g., opacity and pneumonia), and (3) lack of the ability to utilize multiscale information. To address these issues, we propose an edge-assisted computing and mask attention based network (called EAM-Net), which consists of an encoder-decoder network, an edge-assisted computing module, and multiple mask attention modules. Based on the encoder-decoder structure, an edge-assisted computing module is first proposed, which integrates the feature maps of the shallow encoding layers for edge prediction, and uses the edge evidence map as a strong cue to guide the lung region segmentation, thereby refining the lung region segmentation boundaries. We further design a mask attention module after each decoding layer, which employs a mask attention operation to make the model focus on lung regions while suppressing the lesion-related artifacts. Besides, a multiscale aggregation loss is proposed to optimize EAM-Net. Extensive experiments on the JSRT, Shenzhen, and Montgomery datasets demonstrate that EAM-Net outperforms existing state-of-the-art lung region segmentation methods.

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RT‐Unet: An advanced network based on residual network and transformer for medical image segmentation

Cited by 18 publications

References 24 publications

Semantic segmentation feature fusion network based on transformer

Semantic segmentation feature fusion network based on transformer

ALFPN: Adaptive Learning Feature Pyramid Network for Small Object Detection

An Edge-Assisted Computing and Mask Attention Based Network for Lung Region Segmentation

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