Medical imaging plays an important role in disease diagnosis, treatment planning, and clinical monitoring. One of the major challenges in medical image analysis is imbalanced training data, in which the class of interest is less than the other classes. A model learned from imbalanced training data is biased towards the majority class. The predicted results of such networks have low sensitivity and high precision. In medical applications, the cost of misclassification of the minority class could be more than the cost of misclassification of the majority class. For example, the risk of not detecting a tumor could be much higher than referring to a healthy subject to a doctor. The problem of learning from imbalanced training samples has been recently addressed in disease classification, tumor detection, and tumor segmentation. Two types of approaches are proposed in the literature: data-level approaches and algorithm-level approaches. The current thesis addresses the problem of learning from imbalanced medical image datasets with both, data-level and algorithmic-level approaches. At data-level, our objective is to balance the data distribution through re-sampling the data space: we propose novel approaches to correct internal bias towards minority classes. These approaches include: synthesize minority class sampling, patient-wise batch normalization, and complementary labels using generative adversarial networks for all. On the other hand, at algorithm-level, we modify the learning algorithm to alleviate the bias towards the majority class. In this regard, we propose different algorithms for cost-sensitive learning, ensemble learning, and mutual learning to deal with highly imbalanced imaging data. We show evidence that the proposed approaches are applicable to different types of medical images of varied sizes on different applications of routine clinical tasks, such as diseases classification and semantic segmentation. Our various implemented algorithms have shown outstanding results on different medical imaging challenges.