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version](https://badge.fury.io/py/DigiPathAI.svg)](https://badge.fury.io/py/DigiPathAI)\n[![PyPI Downloads](https://static.pepy.tech/badge/digipathai)](https://pepy.tech/projects/digipathai)\n[![arXiv](https://img.shields.io/badge/arXiv-2001.00258-\u003cCOLOR\u003e.svg)](https://arxiv.org/abs/2001.00258)\n\n\n# DigiPathAI\nA software application built on top of [openslide](https://openslide.org/) for viewing [whole slide images (WSI)](https://www.ncbi.nlm.nih.gov/pubmed/30307746) and performing pathological analysis \n\n### Citation\nIf you find this reference implementation useful in your research, please consider citing:\n```\n@article{khened2020generalized,\n  title={A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis},\n  author={Khened, Mahendra and Kori, Avinash and Rajkumar, Haran and Srinivasan, Balaji and Krishnamurthi, Ganapathy},\n  journal={arXiv preprint arXiv:2001.00258},\n  year={2020}\n}\n```\n# Features\n- Responsive WSI image viewer \n- State of the art cancer AI pipeline to segment and display the cancerous tissue regions\n\n# Application Overview\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"imgs/demo.gif\"\u003e\n\u003c/p\u003e\n\n# Results\n\u003cp align=\"center\"\u003e\n  \u003cimg width=\"460\" height=\"300\" src=\"imgs/results_1.png\"\u003e\n\u003c/p\u003e\n\n# Installation\nRunning of the AI pipeline requires a GPU and several deep learning modules. However, you can run just the UI as well.\n\n## Just the UI\n### Requirements\n- `openslide`\n- `flask`\n\nThe following command will install only the dependencies listed above.\n```\npip install DigiPathAI\n```\n\n## Entire AI pipeline\n### Requirements\n- `pytorch`\n- `torchvision`\n- `opencv-python`\n- `imgaug`\n- `matplotlib`\n- `scikit-learn`\n- `scikit-image`\n- `tensorflow-gpu \u003e=1.14,\u003c2`\n- `pydensecrf`\n- `pandas`\n- `wget`\n\nThe following command will install the dependencies mentioned\n```\npip install \"DigiPathAI[gpu]\"\n```\n\nBoth installation methods install the same package, just different dependencies. Even if you had installed using the earlier command, you can install the rest of the dependencies manually. \n\n# Usage \n## Local server\nTraverse to the directory containing the openslide images and run the following command.\n```\ndigipathai \u003chost: localhost (default)\u003e \u003cport: 8080 (default)\u003e\n```\n\n## Python API usage\nThe application also has an API which can be used within python to perform the segmentation. \n```\nfrom DigiPathAI.Segmentation import getSegmentation\n\nprediction = getSegmentation(img_path, \n\t\t\tpatch_size  = 256, \n\t\t\tstride_size = 128,\n\t\t\tbatch_size  = 32,\n\t\t\tquick       = True,\n\t\t\ttta_list    = None,\n\t\t\tcrf         = False,\n\t\t\tsave_path   = None,\n\t\t\tstatus      = None)\n```\n\n# Contact\n- Avinash Kori (koriavinash1@gmail.com)\n- Haran Rajkumar (haranrajkumar97@gmail.com)\n\n[![ko-fi](https://www.ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/M4M132RPG)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharanrk%2Fdigipathai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fharanrk%2Fdigipathai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharanrk%2Fdigipathai/lists"}