Add to ORKGThe availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater, the increase in acquired imaging data renders traditional pixel-wise segmentations less feasible. In this paper, we examine the use of a crowdsourcing platform for the distribution of super-pixel weak annotation tasks and collect such annotations from a crowd of non-expert raters. The crowd annotations are subsequently used for training a fully convolutional neural network to address the problem of fetal brain segmentation in T2-weighted MR images. Using this approach we report encouraging results compared to highly targeted, fully supervised methods and potential...
Medical image segmentation is a fundamental and critical step in many image-guided clinical approach...
Accurate annotations of medical images are essential for various clinical applications. The remarkab...
One of the current challenges in applying machine learning to medical images is the difficulty in ob...
To efficiently establish training databases for machine learning methods, collaborative and crowdsou...
Supervised deep neural networks need datasets for training, in which the data need to be annotated b...
Medical image annotation is a major hurdle for developing precise and robust machine-learning models...
Crowdsourcing in pathology has been performed on tasks that are assumed to be manageable by nonexper...
Recent years have seen an increasing use of supervised learning methods for segmentation tasks. Howe...
Analyses of biomedical images often rely on demarcat-ing the boundaries of biological structures (se...
International audienceMost of the current state-of-the-art methods for tumor segmentation are based ...
In recent years, deep learning-based methods have been widely used in the fields of brain image anal...
Machine learning has been widely adopted for medical image analysis in recent years given its promis...
The scarcity of high-quality annotated medical imaging datasets is a major problem that collides wit...
The surge of supervised learning methods for segmentation lately has underscored the critical role o...
The success of deep learning in image recognition is substantially driven by large-scale, well-curat...
Medical image segmentation is a fundamental and critical step in many image-guided clinical approach...
Accurate annotations of medical images are essential for various clinical applications. The remarkab...
One of the current challenges in applying machine learning to medical images is the difficulty in ob...
To efficiently establish training databases for machine learning methods, collaborative and crowdsou...
Supervised deep neural networks need datasets for training, in which the data need to be annotated b...
Medical image annotation is a major hurdle for developing precise and robust machine-learning models...
Crowdsourcing in pathology has been performed on tasks that are assumed to be manageable by nonexper...
Recent years have seen an increasing use of supervised learning methods for segmentation tasks. Howe...
Analyses of biomedical images often rely on demarcat-ing the boundaries of biological structures (se...
International audienceMost of the current state-of-the-art methods for tumor segmentation are based ...
In recent years, deep learning-based methods have been widely used in the fields of brain image anal...
Machine learning has been widely adopted for medical image analysis in recent years given its promis...
The scarcity of high-quality annotated medical imaging datasets is a major problem that collides wit...
The surge of supervised learning methods for segmentation lately has underscored the critical role o...
The success of deep learning in image recognition is substantially driven by large-scale, well-curat...
Medical image segmentation is a fundamental and critical step in many image-guided clinical approach...
Accurate annotations of medical images are essential for various clinical applications. The remarkab...
One of the current challenges in applying machine learning to medical images is the difficulty in ob...