{"action":"created","issue":{"url":"https://api.github.com/repos/jakeret/tf_unet/issues/81","repository_url":"https://api.github.com/repos/jakeret/tf_unet","labels_url":"https://api.github.com/repos/jakeret/tf_unet/issues/81/labels{/name}","comments_url":"https://api.github.com/repos/jakeret/tf_unet/issues/81/comments","events_url":"https://api.github.com/repos/jakeret/tf_unet/issues/81/events","html_url":"https://github.com/jakeret/tf_unet/issues/81","id":246320369,"number":81,"title":"Multi Class Segmentation","user":{"login":"nargeshn","id":30293166,"avatar_url":"https://avatars0.githubusercontent.com/u/30293166?v=4","gravatar_id":"","url":"https://api.github.com/users/nargeshn","html_url":"https://github.com/nargeshn","followers_url":"https://api.github.com/users/nargeshn/followers","following_url":"https://api.github.com/users/nargeshn/following{/other_user}","gists_url":"https://api.github.com/users/nargeshn/gists{/gist_id}","starred_url":"https://api.github.com/users/nargeshn/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/nargeshn/subscriptions","organizations_url":"https://api.github.com/users/nargeshn/orgs","repos_url":"https://api.github.com/users/nargeshn/repos","events_url":"https://api.github.com/users/nargeshn/events{/privacy}","received_events_url":"https://api.github.com/users/nargeshn/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":1,"created_at":"2017-07-28T11:22:42Z","updated_at":"2017-08-02T23:22:10Z","closed_at":null,"body":"I think this question has been asked by other people but I can not find the issue and your response. \r\nI am trying to use U_net for segmentation of medical images. The segmentations contain more than one label. I modified the labels to binary but I am just curious if U-Net can handle the multi_Class segmentation."},"comment":{"url":"https://api.github.com/repos/jakeret/tf_unet/issues/comments/319825366","html_url":"https://github.com/jakeret/tf_unet/issues/81#issuecomment-319825366","issue_url":"https://api.github.com/repos/jakeret/tf_unet/issues/81","id":319825366,"user":{"login":"nargeshn","id":30293166,"avatar_url":"https://avatars0.githubusercontent.com/u/30293166?v=4","gravatar_id":"","url":"https://api.github.com/users/nargeshn","html_url":"https://github.com/nargeshn","followers_url":"https://api.github.com/users/nargeshn/followers","following_url":"https://api.github.com/users/nargeshn/following{/other_user}","gists_url":"https://api.github.com/users/nargeshn/gists{/gist_id}","starred_url":"https://api.github.com/users/nargeshn/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/nargeshn/subscriptions","organizations_url":"https://api.github.com/users/nargeshn/orgs","repos_url":"https://api.github.com/users/nargeshn/repos","events_url":"https://api.github.com/users/nargeshn/events{/privacy}","received_events_url":"https://api.github.com/users/nargeshn/received_events","type":"User","site_admin":false},"created_at":"2017-08-02T23:22:10Z","updated_at":"2017-08-02T23:22:10Z","body":"Thanks for your response!\r\nI am still a bit confused and just want to make sure that I have understood the process correctly.\r\nFor multi class (for example 10) I need to create a mask for every class in every image. So in this case for every image, I need to create 10 masks. As the result, I will have 10 predictions for every image. At the time of classification, for every pixel, I need to choose the class that has the highest probability (highest value for prediction). \r\nPlease correct me if I am wrong.\r\nIn this way, if we have 10 classes but not all images have 10 classes (for example some images have 7 or 8 classes), wouldn't it cause any issue?"}}
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