Recently, there is an increasing demand for automatically detecting anatomical landmarks which provide rich structural information to facilitate subsequent medical image analysis. Current methods related to this task often leverage the power of deep neural networks, while a major challenge in fine tuning such models in medical applications arises from insufficient number of labeled samples. To address this, we propose to regularize the knowledge transfer across source and target tasks through cross-task representation learning. The proposed method is demonstrated for extracting facial natomical landmarks which facilitate the diagnosis of fetal alcohol syndrome. The source and target tasks in this work are face recognition and landmark detection, respectively. The main idea of the proposed method is to retain the feature representations of the source model on the target task data, and to leverage them as an additional source of supervisory signals for regularizing the target model learning, thereby improving its performance under limited training samples. Concretely, we present two approaches for the proposed representation learning by constraining either final or intermediate model features on the target model. Experimental results on a clinical face image dataset demonstrate that the proposed approach works well with few labeled data, and outperforms other compared approaches.
|Title of host publication
|Machine Learning in Medical Imaging - 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Proceedings
|Mingxia Liu, Chunfeng Lian, Pingkun Yan, Xiaohuan Cao
|Number of pages
|Published - 2020
|11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 4 Oct 2020 → 4 Oct 2020
|Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
|11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020
|4/10/20 → 4/10/20
Bibliographical noteFunding Information:
Acknowledgements. This work was done in conjunction with the Collaborative Initiative on Fetal Alcohol Spectrum Disorders (CIFASD), which is funded by grants from the National Institute on Alcohol Abuse and Alcoholism (NIAAA). This work was supported by NIH grant U01AA014809 and EPSRC grant EP/M013774/1.
© 2020, Springer Nature Switzerland AG.
- Anatomical landmark detection
- Knowledge transfer
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)