  We present an automatic method for joint liver lesion segmentation and
classification using a hierarchical fine-tuning framework. Our dataset is
small, containing 332 2-D CT examinations with lesion annotated into 3 lesion
types: cysts, hemangiomas, and metastases. Using a cascaded U-net that performs
segmentation and classification simultaneously, we trained a strong lesion
segmentation model on the dataset of MICCAI 2017 Liver Tumor Segmentation
(LiTS) Challenge. We used the trained weights to fine-tune a slightly modified
model to obtain improved lesion segmentation and classification, on the smaller
dataset. Since pre-training was done with similar data on a related task, we
were able to learn more representative features (especially higher-level
features in the U-Net's encoder), and improve pixel-wise classification
results. We show an improvement of over 10\% in Dice score and classification
accuracy, compared to a baseline model. We further improve the classification
performance by hierarchically freezing the encoder part of the network and
achieve an improvement of over 15\% in Dice score and classification accuracy.
We compare our results with an existing method and show an improvement of 14\%
in the success rate and 12\% in the classification accuracy.
