The scarcity of high-quality annotated medical imaging datasets is a major problem that collides with machine learning applications in the field of medical imaging analysis and impedes its advancement. Self-supervised learning is a recent training paradigm that enables learning robust representations without the need for human annotation which can be considered an effective solution for the scarcity of annotated medical data. This article reviews the state-of-the-art research directions in self-supervised learning approaches for image data with a concentration on their applications in the field of medical imaging analysis. The article covers a set of the most recent self-supervised learning methods from the computer vision field as they are applicable to the medical imaging analysis and categorize them as predictive, generative, and contrastive approaches. Moreover, the article covers 40 of the most recent research papers in the field of self-supervised learning in medical imaging analysis aiming at shedding the light on the recent innovation in the field. Finally, the article concludes with possible future research directions in the field.
The availability of high quality annotated medical imaging datasets is a major problem that collides with machine learning applications in the field of medical imaging analysis and impedes its advancement. Self-supervised learning is a recent training paradigm that enables learning robust representations without the need for human annotation which can be considered as an effective solution for the scarcity in annotated medical data. This article reviews the state-of-the-art research directions in self-supervised learning approaches for image data with concentration on their applications in the field of medical imaging analysis. The article covers a set of the most recent self-supervised learning methods from the computer vision field as they are applicable to the medical imaging analysis and categorize them as predictive, generative and contrastive approaches. Moreover, the article covers (40) of the most recent researches in the field of self-supervised learning in medical imaging analysis aiming at shedding the light on the recent innovation in the field. Ultimately, the article concludes with possible future research directions in the field.
Retina disorders are among the common types of eye disease that occur due to several reasons such as aging, diabetes and premature born. Besides, Optical Coherence Tomography (OCT) is a medical imaging method that serves as a vehicle for capturing volumetric scans of the human eye retina for diagnoses purposes. This research compared two pretraining approaches including Self-Supervised Learning (SSL) and Transfer Learning (TL) to train ResNet34 neural architecture aiming at building computer aided diagnoses tool for retina disorders recognition. In addition, the research methodology employs convolutional auto-encoder model as a generative SSL pretraining method. The research efforts are implemented on a dataset that contains 109,309 retina OCT images with three medical conditions including Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), DRUSEN as well as NORMAL condition. The research outcomes showed better performance in terms of overall accuracy, sensitivity and specificity, namely, 95.2%, 95.2% and 98.4% respectively for SSL ResNet34 in comparison to scores of 90.7%, 90.7% and 96.9% respectively for TL ResNet34. In addition, SSL pretraining approach showed significant reduction in the number of epochs required for training in comparison to both TL pretraining as well as the previous research performed on the same dataset with comparable performance.
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