Cross-Task Representation Learning for Anatomical Landmark Detection

Zeyu Fu*, Jianbo Jiao, Michael Suttie, J. Alison Noble

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsMingxia Liu, Chunfeng Lian, Pingkun Yan, Xiaohuan Cao
PublisherSpringer
Pages583-592
Number of pages10
ISBN (Print)9783030598600
DOIs
Publication statusPublished - 2020
Event11th 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 20204 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12436 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th 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
Country/TerritoryPeru
CityLima
Period4/10/204/10/20

Bibliographical note

Funding 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.

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

  • Anatomical landmark detection
  • Knowledge transfer

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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