Medical Image Classification using Tensorflow

提供方
Coursera Project Network
在此指導項目中,您將:

Import and compile a Residual Convolutional Network (Resnet).

Train a Resnet to identify pleural effusion in chest x-ray (CXR) images.

Use the fully trained Resnet for inference functions identifying effusion.

Clock2 hours
Advanced高級設置
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

The medical imaging industry is set to see 9 and a half billion dollars in growth in just a few years, mostly due to advances in AI imaging technologies. AI integration with medical imaging is expected to gain traction as it enables increased productivity, improved accuracy, and reduced errors in the diagnosis performed by technicians and radiologists. The use of AI will also automate the labor-intensive manual segmentation and enable technicians to identify abnormalities, in turn, accelerating the treatment process. Furthermore, AI platforms are also being developed for hospitals and health systems to help clinicians in making quick decisions and improving patient outcomes. Ultimately, this field of research will benefit from more minds refining the technology. This project will get you started in using Python and Tensorflow/Keras for advanced medical imaging. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

您要培養的技能

  • tensorflow in production
  • image classification
  • health informatics analysis

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  1. Preprocess medical imaging data

  2. Compile a neural network model -Part 1

  3. Compile a neural network model -Part 2

  4. Build and Train a Resnet Model to recognize lung effusion

  5. Making Predictions in Inference

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

常見問題

常見問題

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