際際滷shows by User: JosephPaulCohen / http://www.slideshare.net/images/logo.gif 際際滷shows by User: JosephPaulCohen / Thu, 20 Dec 2018 16:53:53 GMT 際際滷Share feed for 際際滷shows by User: JosephPaulCohen A survey of deep learning approaches to medical applications /slideshow/a-survey-of-deep-learning-approaches-to-medical-applications/126366886 asurveyofdeeplearningapproachestomedicalapplications212018-181220165353
This talk will cover various medical applications of deep learning including tumor segmentation in histology slides, MRI, CT, and X-Ray data. Also, more complicated tasks such as cell counting where the challenge is to count how many objects are in an image. It will also cover generative adversarial networks and how they can be used for medical applications. This presentation is accessible to non-doctors and non-computer scientists. ]]>
This talk will cover various medical applications of deep learning including tumor segmentation in histology slides, MRI, CT, and X-Ray data. Also, more complicated tasks such as cell counting where the challenge is to count how many objects are in an image. It will also cover generative adversarial networks and how they can be used for medical applications. This presentation is accessible to non-doctors and non-computer scientists. ]]> Thu, 20 Dec 2018 16:53:53 GMT /slideshow/a-survey-of-deep-learning-approaches-to-medical-applications/126366886 JosephPaulCohen@slideshare.net(JosephPaulCohen) A survey of deep learning approaches to medical applications JosephPaulCohen This talk will cover various medical applications of deep learning including tumor segmentation in histology slides, MRI, CT, and X-Ray data. Also, more complicated tasks such as cell counting where the challenge is to count how many objects are in an image. It will also cover generative adversarial networks and how they can be used for medical applications. This presentation is accessible to non-doctors and non-computer scientists. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/asurveyofdeeplearningapproachestomedicalapplications212018-181220165353-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This talk will cover various medical applications of deep learning including tumor segmentation in histology slides, MRI, CT, and X-Ray data. Also, more complicated tasks such as cell counting where the challenge is to count how many objects are in an image. It will also cover generative adversarial networks and how they can be used for medical applications. This presentation is accessible to non-doctors and non-computer scientists.
A survey of deep learning approaches to medical applications from Joseph Paul Cohen PhD
]]> 624 1 https://cdn.slidesharecdn.com/ss_thumbnails/asurveyofdeeplearningapproachestomedicalapplications212018-181220165353-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Distribution Matching Losses Can Hallucinate Features in Medical Image Translation /slideshow/distribution-matching-losses-can-hallucinate-features-in-medical-image-translation/126366475 miccai2018-cohen-181220165026
MICCAI2018 presentation about why image translation (via distribution matching) should not be used for direct interpretation. ArXiv paper here: https://arxiv.org/abs/1805.08841]]>
MICCAI2018 presentation about why image translation (via distribution matching) should not be used for direct interpretation. ArXiv paper here: https://arxiv.org/abs/1805.08841]]> Thu, 20 Dec 2018 16:50:26 GMT /slideshow/distribution-matching-losses-can-hallucinate-features-in-medical-image-translation/126366475 JosephPaulCohen@slideshare.net(JosephPaulCohen) Distribution Matching Losses Can Hallucinate Features in Medical Image Translation JosephPaulCohen MICCAI2018 presentation about why image translation (via distribution matching) should not be used for direct interpretation. ArXiv paper here: https://arxiv.org/abs/1805.08841 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/miccai2018-cohen-181220165026-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> MICCAI2018 presentation about why image translation (via distribution matching) should not be used for direct interpretation. ArXiv paper here: https://arxiv.org/abs/1805.08841
Distribution Matching Losses Can Hallucinate Features in Medical Image Translation from Joseph Paul Cohen PhD
]]> 81 1 https://cdn.slidesharecdn.com/ss_thumbnails/miccai2018-cohen-181220165026-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Clinical data successes using machine learning (word2vec, RNN) /slideshow/clinical-data-successes-using-machine-learning-word2vec-rnn/126366314 asurveyofclinicaldatasuccessesusingmachinelearningandword2ec-181220164904
This talk will cover approaches for working with electronic health records (EHR) using machine learning. Including unsupervised representation learning for medical codes, visits, and patients. Working with time series events and observation using MLPs and RNNs. Also, predicting patient outcomes in terms of survival and other medical events.]]>
This talk will cover approaches for working with electronic health records (EHR) using machine learning. Including unsupervised representation learning for medical codes, visits, and patients. Working with time series events and observation using MLPs and RNNs. Also, predicting patient outcomes in terms of survival and other medical events.]]> Thu, 20 Dec 2018 16:49:04 GMT /slideshow/clinical-data-successes-using-machine-learning-word2vec-rnn/126366314 JosephPaulCohen@slideshare.net(JosephPaulCohen) Clinical data successes using machine learning (word2vec, RNN) JosephPaulCohen This talk will cover approaches for working with electronic health records (EHR) using machine learning. Including unsupervised representation learning for medical codes, visits, and patients. Working with time series events and observation using MLPs and RNNs. Also, predicting patient outcomes in terms of survival and other medical events. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/asurveyofclinicaldatasuccessesusingmachinelearningandword2ec-181220164904-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This talk will cover approaches for working with electronic health records (EHR) using machine learning. Including unsupervised representation learning for medical codes, visits, and patients. Working with time series events and observation using MLPs and RNNs. Also, predicting patient outcomes in terms of survival and other medical events.
Clinical data successes using machine learning (word2vec, RNN) from Joseph Paul Cohen PhD
]]> 469 5 https://cdn.slidesharecdn.com/ss_thumbnails/asurveyofclinicaldatasuccessesusingmachinelearningandword2ec-181220164904-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/asurveyofdeeplearningapproachestomedicalapplications212018-181220165353-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/a-survey-of-deep-learning-approaches-to-medical-applications/126366886 A survey of deep learn... https://cdn.slidesharecdn.com/ss_thumbnails/miccai2018-cohen-181220165026-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/distribution-matching-losses-can-hallucinate-features-in-medical-image-translation/126366475 Distribution Matching ... https://cdn.slidesharecdn.com/ss_thumbnails/asurveyofclinicaldatasuccessesusingmachinelearningandword2ec-181220164904-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/clinical-data-successes-using-machine-learning-word2vec-rnn/126366314 Clinical data successe...