2 research outputs found
Hierarchical Fine-Tuning for joint Liver Lesion Segmentation and Lesion Classification in CT
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.Comment: Accepted to IEEE EMBC 201
Joint Liver Lesion Segmentation and Classification via Transfer Learning
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.Comment: Accepted to MIDL 202