{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-to-read-chest-x-rays-recurrent","title":"Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation","arxiv_id":"1603.08486","date":"2016-03-28","proceeding":"CVPR 2016 6","authors":["Hoo-chang Shin","Kirk Roberts","Le Lu","Dina Demner-Fushman","Jianhua Yao","Ronald M. Summers"],"abstract":"Despite the recent advances in automatically describing image contents, their\napplications have been mostly limited to image caption datasets containing\nnatural images (e.g., Flickr 30k, MSCOCO). In this paper, we present a deep\nlearning model to efficiently detect a disease from an image and annotate its\ncontexts (e.g., location, severity and the affected organs). We employ a\npublicly available radiology dataset of chest x-rays and their reports, and use\nits image annotations to mine disease names to train convolutional neural\nnetworks (CNNs). In doing so, we adopt various regularization techniques to\ncircumvent the large normal-vs-diseased cases bias. Recurrent neural networks\n(RNNs) are then trained to describe the contexts of a detected disease, based\non the deep CNN features. Moreover, we introduce a novel approach to use the\nweights of the already trained pair of CNN/RNN on the domain-specific\nimage/text dataset, to infer the joint image/text contexts for composite image\nlabeling. Significantly improved image annotation results are demonstrated\nusing the recurrent neural cascade model by taking the joint image/text\ncontexts into account.","url_abs":"http://arxiv.org/abs/1603.08486v1","url_pdf":"http://arxiv.org/pdf/1603.08486v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-to-read-chest-x-rays-recurrent","repo_url":"https://github.com/pvtien96/CXRAbnormalityLocalization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1603.08486","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1603.08486"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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