{"id":17614850,"url":"https://github.com/gsarti/cancer-detection","last_synced_at":"2025-05-08T03:17:42.200Z","repository":{"id":109884718,"uuid":"187853954","full_name":"gsarti/cancer-detection","owner":"gsarti","description":"Team Capybara final project \"Histopathologic Cancer Detection\" for the Statistical Machine Learning course @ University of Trieste","archived":false,"fork":false,"pushed_at":"2019-06-28T16:19:29.000Z","size":79776,"stargazers_count":9,"open_issues_count":0,"forks_count":4,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-05-08T03:17:34.840Z","etag":null,"topics":["cancer","cancer-detection","capsule-network","capsule-networks","convolutional-neural-networks","data-science","dssc","healthcare","image-segmentation","random-forest","university-of-trieste","university-project","unsupervised-clustering"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/gsarti.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2019-05-21T14:27:37.000Z","updated_at":"2024-12-12T13:08:13.000Z","dependencies_parsed_at":null,"dependency_job_id":"8ac57e6a-de83-4f55-8a5a-3083a4b43ba2","html_url":"https://github.com/gsarti/cancer-detection","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gsarti%2Fcancer-detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gsarti%2Fcancer-detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gsarti%2Fcancer-detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gsarti%2Fcancer-detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/gsarti","download_url":"https://codeload.github.com/gsarti/cancer-detection/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252990026,"owners_count":21836669,"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":["cancer","cancer-detection","capsule-network","capsule-networks","convolutional-neural-networks","data-science","dssc","healthcare","image-segmentation","random-forest","university-of-trieste","university-project","unsupervised-clustering"],"created_at":"2024-10-22T18:45:29.431Z","updated_at":"2025-05-08T03:17:42.137Z","avatar_url":"https://github.com/gsarti.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"![header](img/header.png)\n\n## Description\n\n\"Histopathologic Cancer Detection\" is developed by Team Capybara ([gsarti](https://github.com/gsarti), [stinco](https://github.com/stinco), [andrealorenzon](https://github.com/andrealorenzon)) as final project for the Statistical Machine Learning course held by Prof. Luca Bortolussi at University of Trieste.\n\nThe project is based on the Kaggle competition [\"Histopathologic Cancer Detection\"](https://www.kaggle.com/c/histopathologic-cancer-detection), in which participants create an algorithm to identify metastatic cancer in small image patches taken from larger digital pathology scans.\n\n## Data\n\nThe data are a slightly modified version of the **PatchCamelyon (PCam)** [benchmark dataset](https://github.com/basveeling/pcam) in which duplicates generated by probabilistic sampling were removed.\n\n\u003e **PCam** packs the clinically-relevant task of metastasis detection into a straight-forward binary image classification task, akin to CIFAR-10 and MNIST. Models can easily be trained on a single GPU in a couple hours, and achieve competitive scores in the Camelyon16 tasks of tumor detection and whole-slide image diagnosis. Furthermore, the balance between task-difficulty and tractability makes it a prime suspect for fundamental machine learning research on topics as active learning, model uncertainty, and explainability.\n\nThe data are provided under the [CC0 License](https://choosealicense.com/licenses/cc0-1.0/). They can be found in the `data` folder.\n\n## Approaches\n\nWe compared three approaches for this classification task:\n\n* Unsupervised segmentation of cellules nuclei followed by a random forest on full-image cell statistics.\n\n* DenseNet-169 convolutional neural network with pretrained weights and adaptive learning rate, based on the top Kaggle kernel for the challenge.\n\n* Capsule networks for tumor detection\n\nMore information on the project and resources can be found in our [project presentation](https://github.com/gsarti/cancer-detection/blob/master/HCD_Presentation.pdf).","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgsarti%2Fcancer-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgsarti%2Fcancer-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgsarti%2Fcancer-detection/lists"}