Add to ORKGInternational audienceWe propose a novel Active Learning framework capable to train effectively a convolutional neural network for semantic segmentation of medical imaging, with a limited amount of training labeled data. Our contribution is a practical Cost-Effective Active Learning approach using dropout at test time as Monte Carlo sampling to model the pixel-wise uncertainty and to analyze the image information to improve the training performance. The source code of this project is available at this https URL :https://marc-gorriz.github.io/CEAL-Medical-Image-Segmentation/
Abstract A deep convolution neural network image segmentation model based on a cost-effective active...
Deep learning models have achieved state-of-the-art performance in semantic image segmentation, but ...
Deep neural networks, in particular convolutional networks, have rapidly become a popular choice for...
We propose a novel Active Learning framework capable to train effectively a convolutional neural net...
We propose a novel Active Learning framework capable to train effectively a convolutional neural net...
This thesis proposes a novel active learning framework capable to train effectively a convolutional ...
Over the last decade, deep learning has achieved tremendous progress in many fields. However, the p...
Acquiring medical images and their segmentation labels is often time-consuming and labor-intensive. ...
Training robust deep learning (DL) systems for medical image classification or segmentation is chall...
In recent years, development of Convolutional Neural Networks has enabled high performing semantic s...
Abstract Purpose This study focuses on assessing the performance of active learning techniques to tr...
Even though active learning forms an important pillar of machine learning, deep learning tools are n...
Even though active learning forms an important pillar of machine learning, deep learning tools are n...
In many real-world tasks of image classification, limited amounts of labeled data are available to t...
Using deep learning, we now have the ability to create exceptionally good semantic segmentation syst...
Abstract A deep convolution neural network image segmentation model based on a cost-effective active...
Deep learning models have achieved state-of-the-art performance in semantic image segmentation, but ...
Deep neural networks, in particular convolutional networks, have rapidly become a popular choice for...
We propose a novel Active Learning framework capable to train effectively a convolutional neural net...
We propose a novel Active Learning framework capable to train effectively a convolutional neural net...
This thesis proposes a novel active learning framework capable to train effectively a convolutional ...
Over the last decade, deep learning has achieved tremendous progress in many fields. However, the p...
Acquiring medical images and their segmentation labels is often time-consuming and labor-intensive. ...
Training robust deep learning (DL) systems for medical image classification or segmentation is chall...
In recent years, development of Convolutional Neural Networks has enabled high performing semantic s...
Abstract Purpose This study focuses on assessing the performance of active learning techniques to tr...
Even though active learning forms an important pillar of machine learning, deep learning tools are n...
Even though active learning forms an important pillar of machine learning, deep learning tools are n...
In many real-world tasks of image classification, limited amounts of labeled data are available to t...
Using deep learning, we now have the ability to create exceptionally good semantic segmentation syst...
Abstract A deep convolution neural network image segmentation model based on a cost-effective active...
Deep learning models have achieved state-of-the-art performance in semantic image segmentation, but ...
Deep neural networks, in particular convolutional networks, have rapidly become a popular choice for...