Current mobile/web applications related to detection and classification of images are mostly based on client-server architecture, where the image/data is passed to server which performs the inference on data and returns the results back to application. Another scenario can be that current deep models require high end gpus to train and infer. We would like to have cost-effective and efficient model which can precisely detect and classify diseases from images provided and should be capable of running on mobile devices. It would be helpful to radiologist as well as patient which can see results on the go using their mobile itself.
Students are required to create (or research existing) deep models to detect and classify radiology images using low powered devices like mobile.
Required Skills : ML, DL, Python, Qemu (Hardware emulator to emulate low powered boards)
Deliverables : Students should make a POC to run and deep model on mobile (possibly emulator) and explore other optimization techniques for deep models.
Note : More details will follow in coming days, but students can explore it and come up with their opinions/solutions.