Add to ORKGIn this paper we propose a patch sampling strategy based on sequential Monte-Carlo methods for Whole Slide Image classification in the context of Multiple Instance Learning and show its capability to achieve high generalization performance on the differentiation between sun exposed and not sun exposed pieces of skin tissue.Postprint (published version
When using deep learning models for reconstruction of one path per pixel Monte Carlo path traced ima...
International audienceAutomated and accurate classification of Whole Slide Image (WSI) is of great s...
Annotating cancerous regions in whole-slide images (WSIs) plays a critical role in both clinical dia...
In this paper we propose a patch sampling strategy based on sequential Monte-Carlo methods for Whole...
We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution i...
International audienceSince the standardization of Whole Slide Images (WSIs) digitization, the use o...
Survival prediction via training deep neural networks with giga-pixel whole-slide images (WSIs) is c...
Whole slide image (WSI) classification is a fundamental task for the diagnosis and treatment of dise...
Multiple instance learning (MIL) has been increasingly used in the classification of histopathology ...
Whole slide image (WSI) classification often relies on deep weakly supervised multiple instance lear...
International audienceMultiple instance learning (MIL) is the preferred approach for whole slide ima...
Malignant lesions in breast tissue specimen whole slide images (WSIs), may lead to a dangerous diagn...
Malignant lesions in breast tissue specimen whole slide images (WSIs), may lead to a dangerous diagn...
Histological image analysis using deep learningThe classification of high dimensional images is beco...
Denoising has proven to be useful to efficiently generate high-quality Monte Carlo renderings. Tradi...
When using deep learning models for reconstruction of one path per pixel Monte Carlo path traced ima...
International audienceAutomated and accurate classification of Whole Slide Image (WSI) is of great s...
Annotating cancerous regions in whole-slide images (WSIs) plays a critical role in both clinical dia...
In this paper we propose a patch sampling strategy based on sequential Monte-Carlo methods for Whole...
We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution i...
International audienceSince the standardization of Whole Slide Images (WSIs) digitization, the use o...
Survival prediction via training deep neural networks with giga-pixel whole-slide images (WSIs) is c...
Whole slide image (WSI) classification is a fundamental task for the diagnosis and treatment of dise...
Multiple instance learning (MIL) has been increasingly used in the classification of histopathology ...
Whole slide image (WSI) classification often relies on deep weakly supervised multiple instance lear...
International audienceMultiple instance learning (MIL) is the preferred approach for whole slide ima...
Malignant lesions in breast tissue specimen whole slide images (WSIs), may lead to a dangerous diagn...
Malignant lesions in breast tissue specimen whole slide images (WSIs), may lead to a dangerous diagn...
Histological image analysis using deep learningThe classification of high dimensional images is beco...
Denoising has proven to be useful to efficiently generate high-quality Monte Carlo renderings. Tradi...
When using deep learning models for reconstruction of one path per pixel Monte Carlo path traced ima...
International audienceAutomated and accurate classification of Whole Slide Image (WSI) is of great s...
Annotating cancerous regions in whole-slide images (WSIs) plays a critical role in both clinical dia...