{"id":13576818,"url":"https://github.com/secrierlab/HistoMIL","last_synced_at":"2025-04-05T08:33:23.844Z","repository":{"id":169812158,"uuid":"623421816","full_name":"secrierlab/HistoMIL","owner":"secrierlab","description":"A Python package for handling histopathology whole-slide images using multiple instance learning (MIL) techniques.","archived":false,"fork":false,"pushed_at":"2024-05-21T15:40:31.000Z","size":4051,"stargazers_count":27,"open_issues_count":0,"forks_count":7,"subscribers_count":2,"default_branch":"main","last_synced_at":"2024-10-29T18:26:39.981Z","etag":null,"topics":["deep-learning","histopathology-image-analysis"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/secrierlab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2023-04-04T10:42:19.000Z","updated_at":"2024-10-14T12:19:03.000Z","dependencies_parsed_at":"2023-11-06T12:47:49.466Z","dependency_job_id":"81441fe6-5ad7-4e19-9627-5821a34020dc","html_url":"https://github.com/secrierlab/HistoMIL","commit_stats":null,"previous_names":["secrierlab/histomil"],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/secrierlab%2FHistoMIL","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/secrierlab%2FHistoMIL/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/secrierlab%2FHistoMIL/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/secrierlab%2FHistoMIL/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/secrierlab","download_url":"https://codeload.github.com/secrierlab/HistoMIL/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247312065,"owners_count":20918340,"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":["deep-learning","histopathology-image-analysis"],"created_at":"2024-08-01T15:01:14.539Z","updated_at":"2025-04-05T08:33:23.832Z","avatar_url":"https://github.com/secrierlab.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"# HistoMIL\n![HistoMIL](https://github.com/secrierlab/HistoMIL/blob/main/logo.png)\n\n### Author: Shi Pan, UCL Genetics Institute\n\nHistoMIL is a Python package for handling histopathology whole-slide images using multiple instance learning (MIL) techniques. With HistoMIL, you can create MIL datasets, train and evaluate MIL models, and make MIL predictions on new slide images.\n\n## Getting Started\n\nTo use HistoMIL, you first need to create a conda environment with the required dependencies.\n\n### create env with pre-defined file\nYou can do this by importing the env.yml file provided in this repository:\n\n### linux user pre-requisites\n1. Create conda env\n```bash\nconda create -n HistoMIL python=3.9\n```\nThis will create a new environment named histomil, which you can activate with:\n\n```bash\nconda activate HistoMIL\n```\n\n### windows user pre-requisites\n\nWindows (10+)\n1. Download OpenSlide binaries from this page. Extract the folder and add bin and lib subdirectories to Windows system path. If you are using a conda environment you can also copy bin and lib subdirectories to [Anaconda Installation Path]/envs/YOUR ENV/Library/.\n\n2. Install OpenJPEG. The easiest way is to install OpenJpeg is through conda using\n\n```bash\nconda create -n HistoMIL python=3.9\n```\nThis will create a new environment named histomil, which you can activate with:\n\n```bash\nconda activate HistoMIL\n```\n\n```bash\nC:\\\u003e conda install -c conda-forge openjpeg\n```\n\n### macOS user pre-requisites\nOn macOS there are two popular package managers, homebrew and macports.\n\nHomebrew\n```bash\nbrew install openjpeg openslide\n```\nMacPorts\n```bash\nport install openjpeg openslide\n```\n\n### create env manually \n\nThen install openslide and pytorch-gpu with following scripts.\n\n```bash\nconda install -c conda-forge openslide\nconda install pytorch pytorch-cuda=11.7 -c pytorch -c nvidia\n```\n\nNext, install the required Python packages with pip:\n\n```bash\npip install -r requirements.txt\n```\nThis will install all the packages listed in requirements.txt, including HistoMIL itself.\n\n\n## Usage\n\nAll of the examples for using HistoMIL are included in the Notebooks folder. You can open and run these Jupyter notebooks to see how to use HistoMIL for different histopathology tasks.\n\n## Contributing\n\nIf you find a bug or want to suggest a new feature for HistoMIL, please open a GitHub issue in this repository. Pull requests are also welcome!\n\n## License\n\nHistoMIL is released under the GNU-GPL License. See the LICENSE file for more information.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsecrierlab%2FHistoMIL","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsecrierlab%2FHistoMIL","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsecrierlab%2FHistoMIL/lists"}