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

## TL;DR

This paper introduces a hierarchical fine-tuning approach using a cascaded U-net for joint liver lesion segmentation and classification in CT scans, achieving significant improvements with limited data.

## Contribution

The study presents a novel hierarchical fine-tuning framework that leverages pre-trained models for improved joint segmentation and classification of liver lesions in small datasets.

## Key findings

- Over 10% improvement in Dice score and classification accuracy.
- Further 15% improvement by hierarchical freezing of encoder.
- 14% success rate and 12% classification accuracy gains over existing methods.

## Abstract

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.