Context encoder transfer learning approaches for retinal image analysis

During the last years, deep learning techniques have emerged as powerful alternatives to solve biomedical image analysis problems. However, the training of deep neural networks usually needs great amounts of labeled data to be done effectively. This is even more critical in the case of biomedical imaging due to the added difficulty of obtaining data labeled by experienced clinicians. To mitigate the impact of data scarcity, one of the most commonly used strategies is transfer learning. Nevertheless, the success of this approach depends on the effectiveness of the available pre-training techniques for learning from little or no labeled data.