  Transfer learning and joint learning approaches are extensively used to
improve the performance of Convolutional Neural Networks (CNNs). In medical
imaging applications in which the target dataset is typically very small,
transfer learning improves feature learning while joint learning has shown
effectiveness in improving the network's generalization and robustness. In this
work, we study the combination of these two approaches for the problem of liver
lesion segmentation and classification. For this purpose, 332 abdominal CT
slices containing lesion segmentation and classification of three lesion types
are evaluated. For feature learning, the dataset of MICCAI 2017 Liver Tumor
Segmentation (LiTS) Challenge is used. Joint learning shows improvement in both
segmentation and classification results. We show that a simple joint framework
outperforms the commonly used multi-task architecture (Y-Net), achieving an
improvement of 10% in classification accuracy, compared to a 3% improvement
with Y-Net.
