To dive deeper into how AI is used in Medicine, you can’t go wrong with the AI for Medicine online course, offered by Coursera.
This tutorial is partly based in the nipy and 3D documentations for medical images and Dicom files. In the end, I used already processed data from an ML competition (and not from a messy hospital), so somebody else did the dirty work for me. However, I didn't dive into the particularities of the medical world too much. All of that of course with our under development open source pytorch library called medicalzoo-pytorch. In a previous article, I talked about a common deep learning pipeline applied to multi-modal magnetic resonance datasets. So, the reason that I decided to write this article is to help ML people dive into medical imaging. The market for machine learning in diagnostic imaging will top 2 billion $ by 2023. As an quantitative example of first google search that one can find out: Interestingly, the funding in the AI Healthcare domain is continuously increasing. Now, every multidisciplinary deep learning research project requires domain knowledge such as medical imaging. If you are in this position, or if you would like to know about AI in medical imaging this article is for you.īack in 2017, when I applied for my master’s degree in biomedical engineering everybody asked me why, as I was already obsessed with deep learning. As a result, a lot of misconceptions and confusions are born. You will see people discussing DICOM and coordinate systems you have never heard before. When you dig in medical images you will see different concepts to seem vague and non-intuitive, at least in the beginning. This is the time that you have to look back from a different perspective and start over. Sometimes you think you understand something, but you fail to explain it.
0 kommentar(er)
