Diabetic retinopathy (DR) is a common eye disease and a significant cause of blindness in diabetic patients. Regular screening with fundus photography and timely intervention is the most effective way to manage the disease. The large population of diabetic patients and their massive screening requirements have generated interest in a computer-aided and fully automatic diagnosis of DR. Deep neural networks, on the other hand, have brought many breakthroughs in various tasks in the recent years. To automate the diagnosis of DR and provide appropriate suggestions to DR patients, we have built a dataset of DR fundus images that have been labeled by the proper treatment method that is required. Using this dataset, we trained deep convolutional neural network models to grade the severities of DR fundus images. We were able to achieve an accuracy of 88.72% for a four-degree classification task in the experiments. We deployed our models on a cloud computing platform and provided pilot DR diagnostic services for several hospitals; in the clinical evaluation, the system achieved a consistency rate of 91.8% with ophthalmologists, demonstrating the effectiveness of our work. INDEX TERMS Diabetic retinopathy, automatic diagnosis, deep neural networks.

Cell tracking plays crucial role in biomedical and computer vision areas. As cells generally have frequent deformation activities and small sizes in microscope image, tracking the non-rigid and non-significant cells is quite difficult in practice. Traditional visual tracking methods have good performances on tracking rigid and significant visual objects, however, they are not suitable for cell tracking problem. In this paper, a novel cell tracking method is proposed by using Convolutional Neural Networks (CNNs) as well as Multi-Task Learning (MTL) techniques. The CNNs learn robust cell features and MTL improves the generalization performance of the tracking. The proposed cell tracking method consists of a particle filter motion model, a multi-task learning observation model, and an optimized model update strategy. In the training procedure, the cell tracking is divided into an online tracking task and an accompanying classification task using the MTL technique. The observation model is trained by building a CNN to learn robust cell features. The tracking procedure is started by assigning the cell position in the first frame of a microscope image sequence. Then, the particle filter model is applied to produce a set of candidate bounding boxes in the subsequent frames. The trained observation model provides the confidence probabilities corresponding to all of the candidates and selects the candidate with the highest probability as the final prediction. Finally, an optimized model update strategy is proposed to enable the multitask observation model for the variation of the tracked cell over the entire tracking procedure. The performance and robustness of the proposed method is analyzed by comparing with other commonly-used methods. Experimental results demonstrate that the proposed method has good performance to the cell tracking problem.

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