Other

项目介绍

AiAiHealthcare/ProjectAiAi 是一个托管在 GitHub 上的开源项目,主要使用 Dockerfile 编写,采用 Other 许可证,目前获得 113 个 Star、24 次 Fork。

仓库全名
AiAiHealthcare/ProjectAiAi
开源许可证
Other
Star 数
113
Fork 数
24
未关闭 Issue
11

README 摘要

Project AiAi AiAi.care project is teaching computers to "see" chest X-rays and interpret them how a human Radiologist would. We are using 700,000 labeled Chest X-Rays + Deep Learning to build an FDA 💊 approved, open-source screening tool for Tuberculosis and Lung Cancer. After an MRMC clinical trial, AiAi CAD will be distributed for free to emerging nations, charitable hospitals, and organizations like WHO 🌏 Our…

完整内容见 GitHub 原始 README 或本页 。

常见问题

ProjectAiAi 用什么语言开发?

主要使用 Dockerfile 编写,采用 Other 许可证,目前有 113 个 Star。

ProjectAiAi 可以免费商用吗?

仓库标注的许可证为 Other,商用前请先阅读仓库 LICENSE 文件确认授权范围。

如何获取 ProjectAiAi 的源码?

可以直接克隆仓库:git clone https://github.com/AiAiHealthcare/ProjectAiAi.git,默认分支为 master;安装与运行步骤见仓库 README。

ProjectAiAi 最近还在维护吗?

仓库最近一次更新为 2020年9月21日,当前有 11 个未关闭 Issue;Star 数为 113,可作为社区活跃度的参考。

关于

AiAi.care project is teaching computers to "see" chest X-rays and interpret them how a human Radiologist would. We are using 700,000 Chest X-Rays + Deep Learning to build an FDA 💊 approved, open-source screening tool for Tuberculosis and Lung Cancer. After an MRMC clinical trial, AiAi CAD will be distributed for free to emerging nations, charitable hospitals, and organizations like WHO 🌏 We will also release our pretrained models and weights as Medical Imagenet.

描述取自 GitHub 官方 API,中文内容由 GitRead 中文社区 编辑团队整理。

简要分析

热度分
188
价值分
77
活跃状态
活跃

语言占比

Dockerfile
Python

Release

README

Project AiAi

GitHub Commits Count

AiAi.care project is teaching computers to “see” chest X-rays and interpret them how a human Radiologist would. We are using 700,000 labeled Chest X-Rays + Deep Learning to build an FDA 💊 approved, open-source screening tool for Tuberculosis and Lung Cancer. After an MRMC clinical trial, AiAi CAD will be distributed for free to emerging nations, charitable hospitals, and organizations like WHO 🌏

Our secondary goal is to open-source ‘Medical Imagenet’ pretrained models and weights. Medical data is hard to obtain, so many current Radiology-AI papers rely on transfer-learning ImageNet weights. There are significant differences between ImageNet images (color, low-res, high-contrast) and Radiology images (grayscale, high-res, low-contrast), so we believe that our Medical Imagenet weights will improve sensitivity and specificity across the board for future research.

If you are looking for a non-technical introduction to Project AiAi, please click here to visit https://AiAi.care website. In case you were wondering about the project’s name, AiAi stands for AI Augmented Imaging.

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Project Milestones

Here are major milestones for the project and target completion dates:

  1. :white_check_mark: ~200,000 X-Ray and CT Data Loading, Cleaning (Completed Feb 1, 2017)
  2. :white_check_mark: Deep Learning Docker Image (PyTorch, HIPAA) (Completed Feb 28, 2017 )
  3. :white_check_mark: Data Augmentation Tests : March 30, 2017 (Completed May 2017)
  4. :white_check_mark: Level 1 Models : April 12, 2017 (Completed May 2017)
  5. :white_check_mark: HIPAA IT Audit and Validation : December 30, 2017 (Internal Audit Completed 2017)
  6. :white_check_mark: We got :sparkles:500,000+:sparkles: additional X-ray images! Merging this with original data. (Completed May 2020)
  7. :soon: Experiements with DL architectures, activations, and augmentation: (In Progress, ETA Q1 2021)
  8. Level 2/3 model ensembles and differential privacy (Detection, Classification) : (In Progress, ETA Q2 2021)
  9. Build mobile-friendly front-end for AiAi CAD (Computer Aided Detection) : Q2 2021
  10. PACS / VNA / DICOM / HL7 / EHR ingestion engine Q3 2021
  11. MRMC clinical validation for FDA application : Q3 2021

If you are a Machine Learning maestro, Kaggle king, or HL7 hacker then please check out our KANBAN project tracker here. You can donate your time and expertise by contributing to any of the issues/tasks pinned on the KANBAN board.

Project AiAi is the first effort of its kind to donate an open-source, medical algorithm to the world. This presents some interesting legal challenges, so we have set up a wiki page where lawyers can donate their time and advice for Project AiAi.

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