  Diabetic Retinopathy (DR) is a non-negligible eye disease among patients with
Diabetes Mellitus, and automatic retinal image analysis algorithm for the DR
screening is in high demand. Considering the resolution of retinal image is
very high, where small pathological tissues can be detected only with large
resolution image and large local receptive field are required to identify those
late stage disease, but directly training a neural network with very deep
architecture and high resolution image is both time computational expensive and
difficult because of gradient vanishing/exploding problem, we propose a
\textbf{Multi-Cell} architecture which gradually increases the depth of deep
neural network and the resolution of input image, which both boosts the
training time but also improves the classification accuracy. Further,
considering the different stages of DR actually progress gradually, which means
the labels of different stages are related. To considering the relationships of
images with different stages, we propose a \textbf{Multi-Task} learning
strategy which predicts the label with both classification and regression.
Experimental results on the Kaggle dataset show that our method achieves a
Kappa of 0.841 on test set which is the 4-th rank of all state-of-the-arts
methods. Further, our Multi-Cell Multi-Task Convolutional Neural Networks
(M$^2$CNN) solution is a general framework, which can be readily integrated
with many other deep neural network architectures.
