{"id":13745482,"url":"https://github.com/paras42/Hello_World_Deep_Learning","last_synced_at":"2025-05-09T06:30:50.552Z","repository":{"id":218536913,"uuid":"116182971","full_name":"paras42/Hello_World_Deep_Learning","owner":"paras42","description":"Hello World Introduction to Deep Learning for Medical Image Classification","archived":false,"fork":false,"pushed_at":"2024-04-25T13:56:47.000Z","size":13938,"stargazers_count":32,"open_issues_count":0,"forks_count":22,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-11-15T18:38:08.336Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/paras42.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-01-03T21:19:06.000Z","updated_at":"2024-07-13T04:58:06.000Z","dependencies_parsed_at":null,"dependency_job_id":"21b665fa-e98c-4f8d-b7c3-b6fe88f22093","html_url":"https://github.com/paras42/Hello_World_Deep_Learning","commit_stats":null,"previous_names":["paras42/hello_world_deep_learning"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/paras42%2FHello_World_Deep_Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/paras42%2FHello_World_Deep_Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/paras42%2FHello_World_Deep_Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/paras42%2FHello_World_Deep_Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/paras42","download_url":"https://codeload.github.com/paras42/Hello_World_Deep_Learning/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253205859,"owners_count":21871158,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-03T06:00:19.514Z","updated_at":"2025-05-09T06:30:45.541Z","avatar_url":"https://github.com/paras42.png","language":"Jupyter Notebook","funding_links":[],"categories":["Classification"],"sub_categories":[],"readme":"# Hello_World_Deep_Learning\n\nAuthor: Paras Lakhani, paras.lakhani@jefferson.edu\n\nMore details and a step-by-step guide for the tutorial can be found in the Journal of Digital Imaging Publication (DOI: 10.1007/s10278-018-0079-6; https://pubmed.ncbi.nlm.nih.gov/29725961/), which is the official journal of the Society of Imaging Informatics in Medicine (SIIM).\n\nThis is a high-level introduction into practical machine learning for purposes of medical image classification. \n\nIn this tutorial, we use the Tensorflow framework and the Keras library, which a high-level application programming interface that simplifies working with Tensorflow.\n\nWe hope that this tutorial will spark interest and provide a basic starting point for those interested in machine learning in regard to medical imaging. \n\nA Jupyter ipython notebook is provided called \"HelloWorldDeepLearning.ipynb\"\n\nWe provide 75 images, 38 are chest X-rays, and 37 are abdominal X-rays. These de-identified PNGs obtained from openI, https://openi.nlm.nih.gov/, a searchable online repository of medical images from published PubMed Central articles\n\nThe goal of this tutorial is to build a deep learning classifier to accurately differentiate between the two.\n\nYou'll need a computer with the following installed:\n\n1) Tensorflow (https://www.tensorflow.org)\n2) Keras library (https://keras.io)\n3) Jupyter (http://jupyter.org)\n4) Download the x-rays provided in .zip file \n\nTo make things easier, there is a convenient SIIM docker that has Tensorflow / Keras / Jupyterlab already installed, located here: https://github.com/ImagingInformatics/machine-learning/tree/master/docker-keras-tensorflow-python3-jupyter\n\nAfter your environment is set up, open the ipython notebook, and run the code!\n\n\n \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fparas42%2FHello_World_Deep_Learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fparas42%2FHello_World_Deep_Learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fparas42%2FHello_World_Deep_Learning/lists"}