2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDBIO-2.11
Paper Title Unsupervised Domain Alignment based Open Set Structural Recognition of Macromolecules Captured by Cryo-Electron Tomography
Authors Yuchen Zeng, Gregory Howe, Carnegie Mellon University, United States; Kai Yi, King Abdullah University of Science and Technology, Saudi Arabia; Xiangrui Zeng, Carnegie Mellon University, United States; Jing Zhang, University of California, Irvine, United States; Yi-Wei Chang, University of Pennsylvania, United States; Min Xu, Carnegie Mellon University, United States
SessionBIO-2: Biomedical Signal Processing 2
LocationArea D
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Biomedical Signal Processing: Biological image analysis
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Abstract Cellular cryo-Electron Tomography (cryo-ET) provides three-dimensional views of structural and spatial information of various macromolecules in cells in a near-native state. Subtomogram classification is a key step for recognizing and differentiating these macromolecular structures. In recent years, deep learning methods have been developed for high-throughput subtomogram classification tasks; however, conventional supervised deep learning methods cannot recognize macromolecular structural classes that do not exist in the training data. This imposes a major weakness since most native macromolecular structures in cells are unknown and consequently, cannot be included in the training data. Therefore, open set learning which can recognize unknown macromolecular structures is necessary for boosting the power of automatic subtomogram classification. In this paper, we propose a method called Margin-based Loss for Unsupervised Domain Alignment (MLUDA) for open set recognition problems where only a few categories of interest are shared between cross-domain data. Through extensive experiments, we demonstrate that MLUDA performs well at cross-domain open-set classification on both public datasets and medical imaging datasets. So our method is of practical importance.