Introduction to medical image processing with Python: CT lung and vessel segmentation without labels
Find out the basics of CT imaging and segment lungs and vessels without labels with 3D medical image processing techniques.
In this article you will learn how the vision transformer works for image classification problems. We distill all the important details you need to grasp along with reasons it can work very well given enough data for pretraining.
What is transfer learning? How can it help us classify and segment different types of medical images? Are pretrained computer vision models useful for medical imaging tasks? How is 2D image classification different from 3D MRI segmentation in terms of transfer learning?
How can deep learning revolutionize medical image analysis beyond segmentation? In this article, we will see a couple of interesting applications in medical imaging such as medical image reconstruction, image synthesis, super-resolution, and registration in medical images
The basic MRI foundations are presented for tensor representation, as well as the basic components to apply a deep learning method that handles the task-specific problems(class imbalance, limited data). Moreover, we present some features of the open source medical image segmentation library. Finally, we discuss our preliminary experimental results and provide sources to find medical imaging data.