{"action":"closed","issue":{"url":"https://api.github.com/repos/greenelab/deep-review/issues/163","repository_url":"https://api.github.com/repos/greenelab/deep-review","labels_url":"https://api.github.com/repos/greenelab/deep-review/issues/163/labels{/name}","comments_url":"https://api.github.com/repos/greenelab/deep-review/issues/163/comments","events_url":"https://api.github.com/repos/greenelab/deep-review/issues/163/events","html_url":"https://github.com/greenelab/deep-review/issues/163","id":196924995,"number":163,"title":"Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification","user":{"login":"agitter","id":3532898,"avatar_url":"https://avatars.githubusercontent.com/u/3532898?v=3","gravatar_id":"","url":"https://api.github.com/users/agitter","html_url":"https://github.com/agitter","followers_url":"https://api.github.com/users/agitter/followers","following_url":"https://api.github.com/users/agitter/following{/other_user}","gists_url":"https://api.github.com/users/agitter/gists{/gist_id}","starred_url":"https://api.github.com/users/agitter/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/agitter/subscriptions","organizations_url":"https://api.github.com/users/agitter/orgs","repos_url":"https://api.github.com/users/agitter/repos","events_url":"https://api.github.com/users/agitter/events{/privacy}","received_events_url":"https://api.github.com/users/agitter/received_events","type":"User","site_admin":false},"labels":[{"id":443706195,"url":"https://api.github.com/repos/greenelab/deep-review/labels/categorize","name":"categorize","color":"d93f0b","default":false},{"id":418330685,"url":"https://api.github.com/repos/greenelab/deep-review/labels/paper","name":"paper","color":"cc317c","default":false}],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":2,"created_at":"2016-12-21T12:48:29Z","updated_at":"2016-12-23T20:30:06Z","closed_at":"2016-12-23T20:30:06Z","body":"https://doi.org/10.1101/095794\r\n\r\n> Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods requires great effort to annotate the training data by costly manual labeling and specialized computational models to detect these annotations during test. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned costly need to annotate the training data. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed deep networks compared to previous work using segmentation and detection annotations in the training."}}
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