Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Hybrid Learning-Based Cell Morphology Profiling Framework for Classifying Cancer Heterogeneity

암의 이질성 분류를 위한 하이브리드 학습 기반 세포 형태 프로파일링 기법

  • Min, Chanhong (Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST)) ;
  • Jeong, Hyuntae (Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST)) ;
  • Yang, Sejung (Department of Biomedical Engineering, Yonsei University) ;
  • Shin, Jennifer Hyunjong (Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST))
  • 민찬홍 (한국과학기술원 기계공학과) ;
  • 정현태 (한국과학기술원 기계공학과) ;
  • 양세정 (연세대학교 보건과학대학 의공학부) ;
  • 신현정 (한국과학기술원 기계공학과)
  • Received : 2021.09.13
  • Accepted : 2021.10.29
  • Published : 2021.10.31
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Abstract

Heterogeneity in cancer is the major obstacle for precision medicine and has become a critical issue in the field of a cancer diagnosis. Many attempts were made to disentangle the complexity by molecular classification. However, multi-dimensional information from dynamic responses of cancer poses fundamental limitations on biomolecular marker-based conventional approaches. Cell morphology, which reflects the physiological state of the cell, can be used to track the temporal behavior of cancer cells conveniently. Here, we first present a hybrid learning-based platform that extracts cell morphology in a time-dependent manner using a deep convolutional neural network to incorporate multivariate data. Feature selection from more than 200 morphological features is conducted, which filters out less significant variables to enhance interpretation. Our platform then performs unsupervised clustering to unveil dynamic behavior patterns hidden from a high-dimensional dataset. As a result, we visualize morphology state-space by two-dimensional embedding as well as representative morphology clusters and trajectories. This cell morphology profiling strategy by hybrid learning enables simplification of the heterogeneous population of cancer.

Keywords

Acknowledgement

본 연구는 KAIST의 2021년 공과대학 석·박사 모험 연구와 한국연구재단의 NRF-2021R1A2C3008408 과제의 지원을 받아 수행하였음.

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