MufiNet: Multiscale Fusion Residual Networks for Medical Image Segmentation

Wang, Chun; Wang, Zhi; Wei, Xi; Zhao, Yang; Bai, Gairui; Wang, Ruimeng; Duan, Meichen

doi:10.1109/ijcnn48605.2020.9207314

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Wang et al [ 40 ] combines features by adding residual connections and merging receptive fields at multiple scales. The result is a network structure that is composed of many combinations of encoder–decoders that can gather more elaborate properties by combining low-level and high-level semantic information.…”

Section: Multiscale-deep-learning Taxonomymentioning

confidence: 99%

A Review on Multiscale-Deep-Learning Applications

Elizar

Zulkifley

Muharar

et al. 2022

Sensors

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In general, most of the existing convolutional neural network (CNN)-based deep-learning models suffer from spatial-information loss and inadequate feature-representation issues. This is due to their inability to capture multiscale-context information and the exclusion of semantic information throughout the pooling operations. In the early layers of a CNN, the network encodes simple semantic representations, such as edges and corners, while, in the latter part of the CNN, the network encodes more complex semantic features, such as complex geometric shapes. Theoretically, it is better for a CNN to extract features from different levels of semantic representation because tasks such as classification and segmentation work better when both simple and complex feature maps are utilized. Hence, it is also crucial to embed multiscale capability throughout the network so that the various scales of the features can be optimally captured to represent the intended task. Multiscale representation enables the network to fuse low-level and high-level features from a restricted receptive field to enhance the deep-model performance. The main novelty of this review is the comprehensive novel taxonomy of multiscale-deep-learning methods, which includes details of several architectures and their strengths that have been implemented in the existing works. Predominantly, multiscale approaches in deep-learning networks can be classed into two categories: multiscale feature learning and multiscale feature fusion. Multiscale feature learning refers to the method of deriving feature maps by examining kernels over several sizes to collect a larger range of relevant features and predict the input images’ spatial mapping. Multiscale feature fusion uses features with different resolutions to find patterns over short and long distances, without a deep network. Additionally, several examples of the techniques are also discussed according to their applications in satellite imagery, medical imaging, agriculture, and industrial and manufacturing systems.

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Section: Multiscale-deep-learning Taxonomymentioning

confidence: 99%

A Review on Multiscale-Deep-Learning Applications

Elizar

Zulkifley

Muharar

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net's potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

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Exploration of Medical Image Super-Resolution in terms of Features and Adaptive Optimization

Ramachandran Nair,

Pichai Pillai,

Narayanan

2023

Deep Learning: Theory, Architectures and Applications in Speech, Image and Language Processing

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Medical image processing takes many steps to capture, process, and convert the images for further analysis. The images are susceptible to distortions due to various factors related to the analysis tools, environment, system-generated faults, and so on. Image enhancement deals with enhancing the quality and resolution of images for accurately analyzing the original information from the images. The primary motivating aspect of research and reconstruction of such high-quality images and their challenges is image super-resolution for image upgrading. This chapter focuses on various image-enhancing strategies in implementing the super-resolution process. In this work, the methodologies of various image-enhancing strategies are explained clearly to provide the parameter selection points, feature comparisons, and performance evaluations that apply to high-resolution image processing. The drawbacks and challenges of each strategy are discussed to investigate the effectiveness of the methodologies. Further research is explored to find hybrid methods on various deep learning architectures to achieve higher accuracy in the field of medical image super-resolution.

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MufiNet: Multiscale Fusion Residual Networks for Medical Image Segmentation

Cited by 3 publications

References 11 publications

A Review on Multiscale-Deep-Learning Applications

A Review on Multiscale-Deep-Learning Applications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

Exploration of Medical Image Super-Resolution in terms of Features and Adaptive Optimization

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