Add to ORKGWe 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/
Segmentation of anatomical structures is a fundamental image analysis task for many applications in ...
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...
International audienceWe propose a novel Active Learning framework capable to train effectively a co...
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...
Training robust deep learning (DL) systems for medical image classification or segmentation is chall...
Deep neural networks, in particular convolutional networks, have rapidly become a popular choice for...
Manual segmentation of stacks of 2D biomedical images (e.g., histology) is a time-consuming task whi...
Abstract Purpose This study focuses on assessing the performance of active learning techniques to tr...
Large, annotated datasets are not widely available in medical image analysis due to the prohibitive ...
Segmentation of anatomical structures is a fundamental image analysis task for many applications in ...
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...
International audienceWe propose a novel Active Learning framework capable to train effectively a co...
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...
Training robust deep learning (DL) systems for medical image classification or segmentation is chall...
Deep neural networks, in particular convolutional networks, have rapidly become a popular choice for...
Manual segmentation of stacks of 2D biomedical images (e.g., histology) is a time-consuming task whi...
Abstract Purpose This study focuses on assessing the performance of active learning techniques to tr...
Large, annotated datasets are not widely available in medical image analysis due to the prohibitive ...
Segmentation of anatomical structures is a fundamental image analysis task for many applications in ...
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...