Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges
Mohammad Hesam Hesamian low, Wenjing Jia, Xiangjian He, Paul Kennedy
Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions.
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- Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges→Long Short-Term MemoryLegacy edge — source detail unavailable
- Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
- Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges→Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…Legacy edge — source detail unavailable
- Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges→A survey on deep learning in medical image analysisLegacy edge — source detail unavailable
- Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges→Deep Learning in Medical Image AnalysisLegacy edge — source detail unavailable
- Deep Learning in Medical Image Analysis→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
- Deep Learning in Medical Image Analysis→Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…Legacy edge — source detail unavailable
- A survey on deep learning in medical image analysis→Long Short-Term MemoryLegacy edge — source detail unavailable
- A survey on deep learning in medical image analysis→Deep Learning in Medical Image AnalysisLegacy edge — source detail unavailable
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
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Topics
| AI in cancer detection | Computer Science |
| Radiomics and Machine Learning in Medical Imaging | Medicine |
| Medical Image Segmentation Techniques | Computer Science |
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