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Typical convolutional neural networks (CNNs) have several millions ofparameters and require a large amount of annotated data to train them. Inmedical applications where training data is hard to come by, thesesophisticated machine learning models are difficult to train. In this paper, wepropose a method to reduce the inherent complexity of CNNs during training byexploiting the significant redundancy that is noticed in the learnt CNNfilters. Our method relies on finding a small set of filters and mixingcoefficients to derive every filter in each convolutional layer at the time oftraining itself, thereby reducing the number of parameters to be trained. Weconsider the problem of 3D lung nodule segmentation in CT images anddemonstrate the effectiveness of our method in achieving good results with onlyfew training examples.

Filter sharing: Efficient learning of parameters for volumetric convolutions