Understanding Transfer Learning For Medical Applications

From interpreting chest x-rays to identifying eye diseases, the domain of transfer learning has found its significance in a variety of standard medical tasks. Therefore, it is extremely important to understand the commonly held assumptions, challenges and other solutions within the realms of transfer learning. A common practice in medical imaging tasks is to start with a large image of a bodily region of interest and identify diseases by identifying the variations in local textures in the images. For example, in retinal fundus images, small red'dots' means presence of microaneurysms and diabetic retinopathy, and in chest x-rays local white opaque patches are signs of consolidation and pneumonia. This is in contrast to natural image datasets like ImageNet, where there is often a clear global subject of the image. There is thus a myriad of open questions unattended such as how much ImageNet feature reuse is helpful for medical images amongst many others.