{"id":473,"date":"2021-11-03T16:00:15","date_gmt":"2021-11-03T16:00:15","guid":{"rendered":"https:\/\/www.creatis.insa-lyon.fr\/~grenier\/?p=473"},"modified":"2022-02-15T21:55:18","modified_gmt":"2022-02-15T21:55:18","slug":"practice-on-medical-image-segmentation-with-unet","status":"publish","type":"post","link":"https:\/\/www.creatis.insa-lyon.fr\/~grenier\/?p=473","title":{"rendered":"Practice on medical image segmentation with UNet"},"content":{"rendered":"\n<p>Here are the hands-on materials to practice deep learning segmentation with UNet (Keras\/TF2):<a href=\"https:\/\/www.creatis.insa-lyon.fr\/~grenier\/wp-content\/uploads\/teaching\/DeepLearning\/TP_UNET_TF2.zip\"> TP_UNET_TF2.zip<\/a><\/p>\n\n\n\n<p>This archive contains jupyter notebooks and necessary functions (i.e. all the code in python). See another post to install TF2\/Keras with conda.<\/p>\n\n\n\n<p>The data are available here: <a href=\"https:\/\/www.creatis.insa-lyon.fr\/~grenier\/wp-content\/uploads\/teaching\/DeepLearning\/dlss21_ho4_data.tar.gz\">dlss21_ho4_data.tar.gz<\/a><\/p>\n\n\n\n<p>The command line to extract the data is:<\/p>\n\n\n\n<p><code> $ tar xzf  dlss21_ho4_data.tar.gz <\/code><\/p>\n\n\n\n<p>The pre-trained model is here : <a href=\"https:\/\/www.creatis.insa-lyon.fr\/~grenier\/wp-content\/uploads\/teaching\/DeepLearning\/Unet_f32_b16_l5_do0.1_Std_BN_input96.h5\">Unet_f32_b16_l5_do0.1_Std_BN_input96.h5<\/a><\/p>\n\n\n\n<p>The following big archive (581MB) contains everything (notebooks, data, pre-trained network) that can be ran out of the box:  <a href=\"https:\/\/www.creatis.insa-lyon.fr\/~grenier\/wp-content\/uploads\/teaching\/DeepLearning\/TP_UNET_TF2_FULL.zip\"> TP_UNET_TF2_FULL.zip<\/a> <\/p>\n","protected":false},"excerpt":{"rendered":"<p>Here are the hands-on materials to practice deep learning segmentation with UNet (Keras\/TF2): TP_UNET_TF2.zip This archive contains jupyter notebooks and necessary functions (i.e. all the code in python). See another post to install TF2\/Keras with conda. The data are available here: dlss21_ho4_data.tar.gz The command line to extract the data is: $ tar xzf dlss21_ho4_data.tar.gz The [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"templates\/template-full-width.php","format":"standard","meta":{"footnotes":""},"categories":[18,6,4],"tags":[],"class_list":["post-473","post","type-post","status-publish","format-standard","hentry","category-deep-learning-teaching","category-image-processing","category-teaching"],"_links":{"self":[{"href":"https:\/\/www.creatis.insa-lyon.fr\/~grenier\/index.php?rest_route=\/wp\/v2\/posts\/473","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.creatis.insa-lyon.fr\/~grenier\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.creatis.insa-lyon.fr\/~grenier\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.creatis.insa-lyon.fr\/~grenier\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.creatis.insa-lyon.fr\/~grenier\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=473"}],"version-history":[{"count":5,"href":"https:\/\/www.creatis.insa-lyon.fr\/~grenier\/index.php?rest_route=\/wp\/v2\/posts\/473\/revisions"}],"predecessor-version":[{"id":558,"href":"https:\/\/www.creatis.insa-lyon.fr\/~grenier\/index.php?rest_route=\/wp\/v2\/posts\/473\/revisions\/558"}],"wp:attachment":[{"href":"https:\/\/www.creatis.insa-lyon.fr\/~grenier\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=473"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.creatis.insa-lyon.fr\/~grenier\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=473"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.creatis.insa-lyon.fr\/~grenier\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=473"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}