It was a nice course. Though it covers basics. A follow-up advanced specilization can be made. Overall, it's sufficient for beginner for an engineer trying to learn application of AI for medical field
Throughout this course, I was able to understand the different medical and deep learning terminology used. Definitely a good course to understand the basic of image classification and segmentation!
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By Nirav S•
Overall it is still a good course and worth doing but I won't expect to be able to clear a job interview in medical machine learning based on this course. It touches many nice topics such as what to do if data is unbalanced, different metrics about evaluating the models. However the part about MRI segmentation seems very rushed. I would consider this as a very basic course and the student would have to spend significant personal time exploring on his/her own to really understand the concepts presented in the class. It wasn't easy for me to get help on some programming assignments when I got stuck a. Moreover, when I didn't get a perfect score on the programming assignments, I don't know where I made the mistakes, which makes it impossible to correct them.
By Erwin J T C•
As a Radiologist from the Philippines who has been desperately trying to find some kind of "grounded center" for all the AI/ML topics I've been studying online, this is a really great way to consolidate what I've learned so far especially for AI applied to Radiology. I've been training models for computer vision (based on free tutorials on-line) but this has definitely given me better insight as to how those models actually work and how they come together from simple numpy arrays, to tensors, layers, and finally into compiled models.... giving me a better appreciation for how activation functions and convolutions actually fit into the development of convolutional neural networks. More power to the team.
By Carlo F•
The course was interesting but did not make me feel ready to apply a DL model on such data. It'a like being in a sandbox all the time: you play, you see things, then you are required to build your own, little, insignificant castle with your little basket, but no more than that. I think that real problems in AI application in this field are not about calculating sensitivity, specificicity or standardazing data, things for whom there are already functions built in libraries. I feel I know more this job, but i wouldn't be ready if i didn't know it yet before.
By Kate S•
I really enjoyed and learned a lot from the material in this course. The lectures were clear and concise. Short lectures made it easy to retain the material. Also helpful were non-graded exercises embedded with the lectures. The graded labs were correct and had helpful hints.
The only improvement that I would want is to have the discussion forums back on Coursera and not on Slack. I found it difficult to search for similar questions on Slack and frequently ran into a limit on the number of messages I could search through.
Overall an excellent course!
By Hossein A•
Overall, it is a good decision to take the course. Although it focuses on practical aspects of the AI in medicine, it falls short explaining the basic CNN architecture for image segmentation or classification. That said if you wish to fully take advantage of the course, spend some time understanding some of the key functions available in the util.py scripts which can be accessed through the notebooks. There, you could benefit from the course and learn interesting implementation stuff if you feel like the assignments are too practical.
By Vinayak N•
This is an amazing course for people who know AI and want to know about it's applications in the healthcare industry. I had fun learning from the instructor Pranav who is concise and delivers lessons comprehensively. Overall an amazing course. Could have asked for more assignments and hands-on stuff, hence I'm being conservative on granting 4-stars only...
By A V A•
Very good course on applying AI for image-based medical diagnosis. Some things that could be improved are : 1. adding content relevant to using AI in non-image based diagnosis 2. could be made more comprehensive with more applications, exercises and theoretical content by extending course duration to a longer time
By Amit P•
The video segments could be made longer to incorporate more information on how the modeling is done. A lot of new information was thrust into the weekly exercises. It would be better if the weekly exercises were a test of what we had learnt. A great course on the whole, anyway. The instructor was very clear.
By Mariathea D•
This is an outstanding course. I am a physician and this has been very helpful in bridging the knowledge gap between what I learned in other deep learning courses and the unique situation of working with medical data. I would however appreciate a deeper dive into how to work with the DICOM format.
By Vishnusai Y•
Introduces the fundamentals of using AI for medical diagnoses. Concepts are clearly explained and the assignments are well framed. More lectures regarding subtle concepts like MRI Image registration and calculation of confidence interval would have made the course more interesting and comprehensive
By Poh S C•
The course serves as an introduction to AI applications on medical diagnosis. The assignments are easy. However, video lectures are missing some minor concepts that suddenly appear in the programming assignment. It is recommended to take this course after you took Deep Learning Specialization.
By Johan T•
Good course but, as often is the case, too much time was spent on fixing small errors in notebooks, such as using the "wrong" function (i.e. np.multiply doesn't work when * does due to the very specific setup of the exercise, even though they are both element-wise multiplication).
By Vignesh S•
A very well structured course that covers most of the practical design challenges of deep learning applications in healthcare sector. A good foundation for people who want to pursue a career as a Machine Learning Engineer for medical diagnosis and/or computer vision.
By Endre S•
Great course! Although the coding exercises focus more on lower level details of matrix manipulation, and not on the parts for selecting a model, building and training it. Most of the model related code is provided if form of utility code or as pretrained weights.
By hasti g•
I enjoyed taking this course. It would be great if assignments could be debuged, I tried downloading the assignments to debug using vscode but some parts of the assignments(datasets or some functions) were not there to be downloaded.
By Chad H•
This was a great course for getting a high-level understanding of AI's applications in medical diagnosis.
The only issue is that the assignments are auto-graded which, coupled with bugs, can make submitting assignments very frustrating.