{"id":18785258,"url":"https://github.com/andreped/pathology-representation-simulation","last_synced_at":"2025-09-02T02:32:30.200Z","repository":{"id":261023279,"uuid":"882792095","full_name":"andreped/pathology-representation-simulation","owner":"andreped","description":"🚀 Create your own simulation of t-SNE on histopathology images!","archived":false,"fork":false,"pushed_at":"2024-11-05T07:41:03.000Z","size":18154,"stargazers_count":4,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-08-31T01:54:43.744Z","etag":null,"topics":["clustering","dimensionality-reduction","histopathology","pathology","representation","simulation","t-sne","tensorboard","tensorboard-projector","training","wsi"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Pathology t-SNE simulation\n[![license](https://img.shields.io/github/license/DAVFoundation/captain-n3m0.svg?style=flat-square)](https://github.com/andreped/pathology-representation-simulation/blob/main/LICENSE.md)\n\u003ca href=\"https://colab.research.google.com/gist/andreped/a58c56104374668c4c632040d24d4064/t-sne-simulation.ipynb#scrollTo=fYm6aF3tIZbM\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e\n\nThis repository contains a simple demonstration of how t-SNE can be used to \nrepresents different tissue types in histopathological images.\n\n\u003cimg src=\"assets/t-sne-simulation-pathology.gif\" title=\"t-SNE\" width=750 style=\"background-color:black\"\u003e\n\n## Stack\n\nFor this demonstration I used Jupyter Notebook to make it easy for others to reproduce.\nI also used the following Python packages:\n* [TensorBoard](https://pypi.org/project/tensorboard/) (with [Embedding Projector](https://www.tensorflow.org/tensorboard/tensorboard_projector_plugin) plugin)\n* [medmnist](https://pypi.org/project/medmnist/) (to access the [MedMNIST](https://medmnist.com) histopathology image dataset)\n* [Pillow](https://pypi.org/project/pillow/)\n* [NumPy](https://pypi.org/project/numpy/)\n\n## Getting started\n\nTo recreate a similar video to the one above, you can simply run the Jupyter Notebook (see under `assets/`).  \nNote that you may need to run the last cell a few times before the TensorBoard dashboard shows.  \nWhen it opens, enable **Spherizie Data** by clicking the checkbox on the left-hand sidebar.  \nThen click the **T-SNE** option underneath. This should start the simulation immediantly.  \nThen just play around with hyperparameters to get a simulation you are pleased with.\n\nFor the actual recording, I used screen recording on my macOS 14.6 using `CMD + SHIFT + 5`\nand selecting a window, but use whichever tool you prefer :]\n\nTo convert recording to GIF, use any video converter like [ezgif](https://ezgif.com/video-to-gif) or similar.\n\n## Citation\n\nIf you use this illustration in a presentation or publication, please cite it using the repository URL.\n\n## Acknowledgements\n\nImplementations were based on the code provided in [this blog post](https://learnopencv.com/t-sne-t-distributed-stochastic-neighbor-embedding-explained/).\n\n## License\n\nThis project has MIT license.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandreped%2Fpathology-representation-simulation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fandreped%2Fpathology-representation-simulation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandreped%2Fpathology-representation-simulation/lists"}