Deep learning for image segmentation
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C. Thangamani
Abstract
Image segmentation, an essential part of computer vision, has perhaps undergone some of the most revolutionary changes through the application of deep learning structures. This work aims at presenting a systematic survey about deep learning methods for image segmentation, with a major attention on state-of-the-art architectures and some of the problems associated with them. Starting with the first heuristic convolutional neural network and the initial U-Net modelU-Net model, the discussion covers features of DeepLab family and the recent attempt to incorporate transformers into segmentation tasks. Problem areas, including computationally expensive methods, loss of small features, and transfer between various datasets, are considered. Future prospects are provided and suggestions for enhancing the efficiency of the mechanism, scalability, and flexibility in the future are also added. The purpose of this chapter was to provide the readers with a comprehensive knowledge and analysis on the state and direction of the current development and potential research topics of image segmentation.
Abstract
Image segmentation, an essential part of computer vision, has perhaps undergone some of the most revolutionary changes through the application of deep learning structures. This work aims at presenting a systematic survey about deep learning methods for image segmentation, with a major attention on state-of-the-art architectures and some of the problems associated with them. Starting with the first heuristic convolutional neural network and the initial U-Net modelU-Net model, the discussion covers features of DeepLab family and the recent attempt to incorporate transformers into segmentation tasks. Problem areas, including computationally expensive methods, loss of small features, and transfer between various datasets, are considered. Future prospects are provided and suggestions for enhancing the efficiency of the mechanism, scalability, and flexibility in the future are also added. The purpose of this chapter was to provide the readers with a comprehensive knowledge and analysis on the state and direction of the current development and potential research topics of image segmentation.
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- List of contributors VII
- Deep learning in computer vision 1
- Deep learning for medical image segmentation 51
- Deep learning for image segmentation 107
- Machine learning algorithm for medical image processing 155
- Machine learning models for predicting anomaly in scanned images 215
- Advanced machine learning models for accurate and efficient anomaly detection in scanned visual data 263
- AI-enhanced diagnostic materials improving sensitivity for disease detection and diagnostics 311
- Machine learning approaches for optimizing the synthesis and functionalization of quantum dots for medical imaging 353
- Machine learning application in tissue engineering: scaffold design 407
- Machine learning approaches to improve electrospun nanofibers’ performance and properties for medical applications 441
- Predictive machine learning models for assessing the long-term stability of biodegradable scaffolds 483
- Customization of medical implants using 3D printing 523
- Index 559
- De Gruyter Series in Advanced Mechanical Engineering
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- List of contributors VII
- Deep learning in computer vision 1
- Deep learning for medical image segmentation 51
- Deep learning for image segmentation 107
- Machine learning algorithm for medical image processing 155
- Machine learning models for predicting anomaly in scanned images 215
- Advanced machine learning models for accurate and efficient anomaly detection in scanned visual data 263
- AI-enhanced diagnostic materials improving sensitivity for disease detection and diagnostics 311
- Machine learning approaches for optimizing the synthesis and functionalization of quantum dots for medical imaging 353
- Machine learning application in tissue engineering: scaffold design 407
- Machine learning approaches to improve electrospun nanofibers’ performance and properties for medical applications 441
- Predictive machine learning models for assessing the long-term stability of biodegradable scaffolds 483
- Customization of medical implants using 3D printing 523
- Index 559
- De Gruyter Series in Advanced Mechanical Engineering
