Deep learning for medical image segmentation
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M. Arthy
Abstract
A revolutionary change has been brought in by the integration of deep learningdeep learning into medical picture segmentation, making anatomical features and diseased areas identification accurate and automated. Here, in this book chapter, authors comprehensively describe the history of various segmentation methods; also, the focus is on the shift from traditional model-based methods to modern deep learning approaches. The discussion also has updates and expansions that address optimization in clinical decision-making and diagnostic imaging with machine learning techniques, such as the hybrid model, generative adversarial networks, and integration of multimodal imagingmultimodal imaging. As always the case with explorative studies, the chapter also looks at practical implementations of deep learning models in clinical environments, by discussing challenges and solutions relating to interpretability, transferability, and regulation. Special emphasis has been placed on how accurate segmentation supports the concept of PPMT by recognizing precision oncology and individualized therapy schedules. The future trends include implementing inter alia, processing in real time, the ability to work with multiomics data, and improvement of a strong algorithm. This work showcases how segmentation techniques developed through deep learning transform medical practice by improving patients’ experience, operation planning, and diagnosticsdiagnostics.
Abstract
A revolutionary change has been brought in by the integration of deep learningdeep learning into medical picture segmentation, making anatomical features and diseased areas identification accurate and automated. Here, in this book chapter, authors comprehensively describe the history of various segmentation methods; also, the focus is on the shift from traditional model-based methods to modern deep learning approaches. The discussion also has updates and expansions that address optimization in clinical decision-making and diagnostic imaging with machine learning techniques, such as the hybrid model, generative adversarial networks, and integration of multimodal imagingmultimodal imaging. As always the case with explorative studies, the chapter also looks at practical implementations of deep learning models in clinical environments, by discussing challenges and solutions relating to interpretability, transferability, and regulation. Special emphasis has been placed on how accurate segmentation supports the concept of PPMT by recognizing precision oncology and individualized therapy schedules. The future trends include implementing inter alia, processing in real time, the ability to work with multiomics data, and improvement of a strong algorithm. This work showcases how segmentation techniques developed through deep learning transform medical practice by improving patients’ experience, operation planning, and diagnosticsdiagnostics.
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
