Medical Image Grading Learning Based on Active and Incremental Fine-Tuning

The development of convolutional neural network has brought great achievements to image classification in recent years. However, the classification performance is good only for natural images rather than medical images. An important reason is that the medical image database used for training is always deficient. So how to use these limited data to acquire more extensive features has become a hot research focus. In this paper, we first update the order and number of the whole training data every time in active and incremental fine-tuning. Then we set different contribution rate for the data selected in our model, which based on the information quantity of the data in training stage and make our model converge steadily. After that, a pre-trained model and our preprocessed datasets are employed, which allows us to further fine-tune our models. The experiments evaluated on two different biomedical datasets shows that our model can achieve promising results.