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Transfer learning and joint learning approaches are extensively used toimprove the performance of Convolutional Neural Networks (CNNs). In medicalimaging applications in which the target dataset is typically very small,transfer learning improves feature learning while joint learning has showneffectiveness in improving the network's generalization and robustness. In thiswork, we study the combination of these two approaches for the problem of liverlesion segmentation and classification. For this purpose, 332 abdominal CTslices containing lesion segmentation and classification of three lesion typesare evaluated. For feature learning, the dataset of MICCAI 2017 Liver TumorSegmentation (LiTS) Challenge is used. Joint learning shows improvement in bothsegmentation and classification results. We show that a simple joint frameworkoutperforms the commonly used multi-task architecture (Y-Net), achieving animprovement of 10% in classification accuracy, compared to a 3% improvementwith Y-Net.

Joint Liver Lesion Segmentation and Classification via Transfer Learning