医学影像分割tricks合集:Deep Learning for Medical Image Segmentation:Tricks,Challenges and Future Directions 飞霜 Slow down to go fast.199 人赞同了该文章 实验非常solid的一篇文章,对比了医学影像分割中各个实验阶段常见的tricks,旨在为以后的工作提供基准,以消除实验结果的不公平比较,详细信息可以查看作者 @...
Segmentation using multi-modality has been widely studied with the development of medical image acquisition systems. Different strategies for image fusion, such as probability theory [1], [2], fuzzy concept [3], [4], believe functions [5], [6], and machine learning [7], [8], [9], [...
Unregistered multiview mammogram analysis with pre-trained deep learning models[C]. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015: 652-660. 7 Greenspan H, Van Ginneken B, Summers RM. Guest editorial deep learning in medical imaging: Overview and future ...
图1 An overview of deep learning methods on medical image segmentation 早期的医学图像分割方法往往依赖于边缘检测、模板匹配技术、统计形状模型、主动轮廓和机器学习等,虽然有大量的方法被报道并在某些情况下取得了成功,但由于特征表示和困难,图像分割仍然是计算机视觉领域中最具挑战性的课题之一,特别是从医学图像中...
In this paper we introduced both automatic brain image segmentation methods to extract the Corpus Callosum (CC), the first based on SLIC using parallel implementation which gives accelerated results over classical SLIC, the second method motivated by using Deep Learning approach. Finally, we compare ...
This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using...
2.2.3. Learning Active Learning 取代手动设计的策略(之前所说的使用置信度低的样本作为informativeness高的样本),通过模型预测选出样本的经验,学习选择样本的策略。 2.3. Fine-tuning vs Retraining 在得到新标注的数据后,为了提升现有模型,是用新增的数据来fine-tuning,还是用所有数据(或者新数据+旧数据的subset)来...
This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using...
Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. A comprehensive thematic survey on medical image segmentation using deep learning techniques is presented. This paper makes two origin...
cov- ers state-of-the-art reviews of deep learning approaches for medical image analysis, including medical image detection/recognition, medical image segmentation, medi- cal image registration, computer aided diagnosis and disease quantification, to name some of the most important addressed problems. ...