@inproceedings{19b855e68c194a70a73a277c280f467c,
title = "Transfer learning from grid-structured data to graph-structured data: Application to diagnosis of depression",
abstract = "Graphs provide a way to study the characteristics of structural and functional connections between different brain regions. Graph convolutional neural networks have the ability to extract intrinsic local characteristics of networks. However, lack of data is a common problem in medical field, holding back the application of deep learning. Although transfer learning is an effective method to improve performance, it is difficult to find natural graph-structured datasets to pre-train deep learning models. To address this problem, we proposed a novel transfer learning method. The method uses grid-structured source data to pre-train a model, and fine-tuned it with graph-structured data in the task of interest. At last, the method is applied to the diagnosis of depression with a total of 83 samples. By comparing the performance between a fine-tuned model and a fully trained model, we tested the effectiveness of the proposal method. And there is a significant improvement in the accuracy of the pre-trained model.",
keywords = "Convolutional neural networks, Deep learning, Fine-tuning, Graph structure, Medical image analysis, Transfer learning",
author = "Jiawei Yang and Shaoping Wang and Xingjian Wang and Rui Liu and Yun Wang and Jian Cui and Yuan Zhou and Jingjing Zhou and Yuan Feng and Lei Feng and Gang Wang",
note = "Publisher Copyright: {\textcopyright} ESREL2020-PSAM15 Organizers.Published by Research Publishing, Singapore.; 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM15 2020 ; Conference date: 01-11-2020 Through 05-11-2020",
year = "2020",
doi = "10.3850/978-981-14-8593-0\_4086-cd",
language = "英语",
isbn = "9789811485930",
series = "Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference",
publisher = "Research Publishing, Singapore",
pages = "1373--1378",
editor = "Piero Baraldi and \{Di Maio\}, Francesco and Enrico Zio",
booktitle = "Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference",
}