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Bewertung und Feedback des Lernenden für Medical Image Classification using Tensorflow von Coursera Project Network

14 Bewertungen
3 Bewertungen

Über den Kurs

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....
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1 - 3 von 3 Bewertungen für Medical Image Classification using Tensorflow

von Feng F

4. Sep. 2021

This course is very useful for students who want to have some real projects to consolidate what they learned about machine learning. But maybe more detailed description about the codes may be even better.

von Christopher B

2. Okt. 2021

There's some good concepts in here, but they're hardly explained.

The code is now outdated, which is an avoidable issue, the requirements.txt file does not seem to rectify the issue either.

Bit frustrating to get to the end of a few hours worth of work and not have the code work (also I had to search for the image files on the internet, they were not provided nor was a link or anywhere to get them from).

von Dineshkumar P

22. Juni 2021

I would not recommend this course to others.

I've hands on experience with medical images for about 10 years and I've good knowledge on Python as well. However this guided project is just not worth the time. The entire project is typing the code without clear explanation of why we are doing it the way or why we are writing the code like that.