Self-Supervised Learning for Medical Imaging

Abstract

Abstract: The role of deep learning is growing quite effectively. As the created models are producing promising accuracy and the early detection and mitigation of diseases is becoming quite easy. As a result, the deep learning algorithm is receiving a variety of interest nowadays for fixing several problems within side the area of scientific imaging. In ophthalmology, one instance is detecting disorder or anomalies by using photos and classifying them into diverse disorder types or severity levels. This sort of project has been finished the use of quite a few machine learning algorithms which have been optimized, in addition to theoretical and empirical approaches. Diabetic Retinopathy is such a disease where in early detection plays a severe role as it could result in imaginative and prescient loss. Diabetic Retinopathy disease recognition has been one of the active and challenging research areas in the field of image processing. Deep learning technique as well as hinders to work with disease recognition and find the accuracy of the model. To create a model in a supervised manner, we need a huge amount of dataset which is very costly. So, as to overcome this problem, we have implemented a self - supervised model for the detection of diabetic retinopathy which works with a very limited dataset. This model is implemented using one of the pretext/proxy task image rotations developed on Dense NET architecture. The model is fine-tuned with the various quantities of subsets of the original dataset and compared internally.