際際滷shows by User: dimalituiev / http://www.slideshare.net/images/logo.gif 際際滷shows by User: dimalituiev / Tue, 20 Nov 2018 01:12:35 GMT 際際滷Share feed for 際際滷shows by User: dimalituiev Preparing Pathology WSI data for Machine Learning Experiments /slideshow/preparing-pathology-wsi-data-for-machine-learning-experiments/123468162 dlituiev-pathvis2018-prepdata-bonus-181120011235
With the advent of convolutional neural networks in recent years, machine learning is becoming increasingly accessible to researchers from non-computer science background. Furthermore, whole-slide imaging (WSI) pathology is gaining acceptance in both research and clinical practice, allowing for more biomedical researchers to apply modern machine learning tools to their data. However, preparing data for machine learning remains a crucial step which requires hands-on expertise. In this presentation we show how to use python open-source tools to load and transform digital slides and their annotation. As an example we will use a set of annotated kidney pathology WSI. We demonstrate how to load slides and annotation and how to save images suitable for a machine learning experiment. ]]>
With the advent of convolutional neural networks in recent years, machine learning is becoming increasingly accessible to researchers from non-computer science background. Furthermore, whole-slide imaging (WSI) pathology is gaining acceptance in both research and clinical practice, allowing for more biomedical researchers to apply modern machine learning tools to their data. However, preparing data for machine learning remains a crucial step which requires hands-on expertise. In this presentation we show how to use python open-source tools to load and transform digital slides and their annotation. As an example we will use a set of annotated kidney pathology WSI. We demonstrate how to load slides and annotation and how to save images suitable for a machine learning experiment. ]]> Tue, 20 Nov 2018 01:12:35 GMT /slideshow/preparing-pathology-wsi-data-for-machine-learning-experiments/123468162 dimalituiev@slideshare.net(dimalituiev) Preparing Pathology WSI data for Machine Learning Experiments dimalituiev With the advent of convolutional neural networks in recent years, machine learning is becoming increasingly accessible to researchers from non-computer science background. Furthermore, whole-slide imaging (WSI) pathology is gaining acceptance in both research and clinical practice, allowing for more biomedical researchers to apply modern machine learning tools to their data. However, preparing data for machine learning remains a crucial step which requires hands-on expertise. In this presentation we show how to use python open-source tools to load and transform digital slides and their annotation. As an example we will use a set of annotated kidney pathology WSI. We demonstrate how to load slides and annotation and how to save images suitable for a machine learning experiment. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dlituiev-pathvis2018-prepdata-bonus-181120011235-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> With the advent of convolutional neural networks in recent years, machine learning is becoming increasingly accessible to researchers from non-computer science background. Furthermore, whole-slide imaging (WSI) pathology is gaining acceptance in both research and clinical practice, allowing for more biomedical researchers to apply modern machine learning tools to their data. However, preparing data for machine learning remains a crucial step which requires hands-on expertise. In this presentation we show how to use python open-source tools to load and transform digital slides and their annotation. As an example we will use a set of annotated kidney pathology WSI. We demonstrate how to load slides and annotation and how to save images suitable for a machine learning experiment.
Preparing Pathology WSI data for Machine Learning Experiments from Dima Lituiev
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