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Automatic detection of liver lesions in CT images poses a great challenge forresearchers. In this work we present a deep learning approach that modelsexplicitly the variability within the non-lesion class, based on priorknowledge of the data, to support an automated lesion detection system. Amulti-class convolutional neural network (CNN) is proposed to categorize inputimage patches into sub-categories of boundary and interior patches, thedecisions of which are fused to reach a binary lesion vs non-lesion decision.For validation of our system, we use CT images of 132 livers and 498 lesions.Our approach shows highly improved detection results that outperform thestate-of-the-art fully convolutional network. Automated computerized tools, asshown in this work, have the potential in the future to support theradiologists towards improved detection.

Modeling the Intra-class Variability for Liver Lesion Detection using a Multi-class Patch-based CNN