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Colorectal liver metastasis is one of most aggressive liver malignancies.While the definition of lesion type based on CT images determines the diagnosisand therapeutic strategy, the discrimination between cancerous andnon-cancerous lesions are critical and requires highly skilled expertise,experience and time. In the present work we introduce an end-to-end deeplearning approach to assist in the discrimination between liver metastases fromcolorectal cancer and benign cysts in abdominal CT images of the liver. Ourapproach incorporates the efficient feature extraction of InceptionV3 combinedwith residual connections and pre-trained weights from ImageNet. Thearchitecture also includes fully connected classification layers to generate aprobabilistic output of lesion type. We use an in-house clinical biobank with230 liver lesions originating from 63 patients. With an accuracy of 0.96 and aF1-score of 0.92, the results obtained with the proposed approach surpassesstate of the art methods. Our work provides the basis for incorporating machinelearning tools in specialized radiology software to assist physicians in theearly detection and treatment of liver lesions.

End-to-End Discriminative Deep Network for Liver Lesion Classification