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We present an automatic method for joint liver lesion segmentation andclassification using a hierarchical fine-tuning framework. Our dataset issmall, containing 332 2-D CT examinations with lesion annotated into 3 lesiontypes: cysts, hemangiomas, and metastases. Using a cascaded U-net that performssegmentation and classification simultaneously, we trained a strong lesionsegmentation model on the dataset of MICCAI 2017 Liver Tumor Segmentation(LiTS) Challenge. We used the trained weights to fine-tune a slightly modifiedmodel to obtain improved lesion segmentation and classification, on the smallerdataset. Since pre-training was done with similar data on a related task, wewere able to learn more representative features (especially higher-levelfeatures in the U-Net's encoder), and improve pixel-wise classificationresults. We show an improvement of over 10\% in Dice score and classificationaccuracy, compared to a baseline model. We further improve the classificationperformance by hierarchically freezing the encoder part of the network andachieve 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.

Hierarchical Fine-Tuning for joint Liver Lesion Segmentation and Lesion Classification in CT