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

arXiv: https://arxiv.org/abs/1907.13409 · HTML: https://mlsanity.com/paper/1907.13409/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2019.

## Built on by (most cited first, 2 of 2)

- [Deep transfer learning for image classification: a survey](https://mlsanity.com/paper/2205.09904/) (2022)
- [Categorizing the Visual Environment and Analyzing the Visual Attention of Dogs](https://mlsanity.com/paper/2311.11988/) (2023)

## Abstract (arXiv)

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.