Add to ORKGNeural networks promise to bring robust, quantitative analysis to medical fields. However, their adoption is limited by the technicalities of training these networks and the required volume and quality of human-generated annotations. To address this gap in the field of pathology, we have created an intuitive interface for data annotation and the display of neural network predictions within a commonly used digital pathology whole-slide viewer. This strategy used a 'human-in-the-loop' to reduce the annotation burden. We demonstrate that segmentation of human and mouse renal micro compartments is repeatedly improved when humans interact with automatically generated annotations throughout the training process. Finally, to show the adaptability ...
Purpose Training deep neural networks usually require a large number of human-annotated data. For o...
In digital pathology, deep learning has been shown to have a wide range of applications, from cancer...
The surge of supervised learning methods for segmentation lately has underscored the critical role o...
Neural networks promise to bring robust, quantitative analysis to medical fields. However, their ado...
Advances in machine learning techniques have been shown to bring benefit for analysing medical image...
Given that neural networks have been widely reported in the research community of medical imaging, w...
Given that neural networks have been widely reported in the research community of medical imaging, w...
Deep learning has been applied successfully to many biomedical image segmentation tasks. However, du...
Artificial intelligence, and more precisely deep learning, has shown remarkable performance in the f...
Manual analysis of medical images such as magnetic resonance imaging (MRI) requires a trained profes...
The emergence of computational pathology comes with a demand to extract more and more information fr...
Reducing the time and storage memory required for scanning whole slide images (WSIs) is crucial. In ...
The digitalization of clinical workflows and the increasing performance of deep learning algorithms ...
With medical imaging playing an increasingly prominent role in the diagnosis of disease, interests i...
Medical image annotation is a major hurdle for developing precise and robust machine-learning models...
Purpose Training deep neural networks usually require a large number of human-annotated data. For o...
In digital pathology, deep learning has been shown to have a wide range of applications, from cancer...
The surge of supervised learning methods for segmentation lately has underscored the critical role o...
Neural networks promise to bring robust, quantitative analysis to medical fields. However, their ado...
Advances in machine learning techniques have been shown to bring benefit for analysing medical image...
Given that neural networks have been widely reported in the research community of medical imaging, w...
Given that neural networks have been widely reported in the research community of medical imaging, w...
Deep learning has been applied successfully to many biomedical image segmentation tasks. However, du...
Artificial intelligence, and more precisely deep learning, has shown remarkable performance in the f...
Manual analysis of medical images such as magnetic resonance imaging (MRI) requires a trained profes...
The emergence of computational pathology comes with a demand to extract more and more information fr...
Reducing the time and storage memory required for scanning whole slide images (WSIs) is crucial. In ...
The digitalization of clinical workflows and the increasing performance of deep learning algorithms ...
With medical imaging playing an increasingly prominent role in the diagnosis of disease, interests i...
Medical image annotation is a major hurdle for developing precise and robust machine-learning models...
Purpose Training deep neural networks usually require a large number of human-annotated data. For o...
In digital pathology, deep learning has been shown to have a wide range of applications, from cancer...
The surge of supervised learning methods for segmentation lately has underscored the critical role o...