2 research outputs found
AITom: Open-source AI platform for cryo-electron tomography data analysis
Cryo-electron tomography (cryo-ET) is an emerging technology for the 3D
visualization of structural organizations and interactions of subcellular
components at near-native state and sub-molecular resolution. Tomograms
captured by cryo-ET contain heterogeneous structures representing the complex
and dynamic subcellular environment. Since the structures are not purified or
fluorescently labeled, the spatial organization and interaction between both
the known and unknown structures can be studied in their native environment.
The rapid advances of cryo-electron tomography (cryo-ET) have generated
abundant 3D cellular imaging data. However, the systematic localization,
identification, segmentation, and structural recovery of the subcellular
components require efficient and accurate large-scale image analysis methods.
We introduce AITom, an open-source artificial intelligence platform for cryo-ET
researchers. AITom provides many public as well as in-house algorithms for
performing cryo-ET data analysis through both the traditional template-based or
template-free approach and the deep learning approach. AITom also supports
remote interactive analysis. Comprehensive tutorials for each analysis module
are provided to guide the user through. We welcome researchers and developers
to join this collaborative open-source software development project.
Availability: https://github.com/xulabs/aitomComment: 2 figure
Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography
Motivation: Cryo-Electron Tomography (cryo-ET) is a 3D bioimaging tool that
visualizes the structural and spatial organization of macromolecules at a
near-native state in single cells, which has broad applications in life
science. However, the systematic structural recognition and recovery of
macromolecules captured by cryo-ET are difficult due to high structural
complexity and imaging limits. Deep learning based subtomogram classification
have played critical roles for such tasks. As supervised approaches, however,
their performance relies on sufficient and laborious annotation on a large
training dataset.
Results: To alleviate this major labeling burden, we proposed a Hybrid Active
Learning (HAL) framework for querying subtomograms for labelling from a large
unlabeled subtomogram pool. Firstly, HAL adopts uncertainty sampling to select
the subtomograms that have the most uncertain predictions. Moreover, to
mitigate the sampling bias caused by such strategy, a discriminator is
introduced to judge if a certain subtomogram is labeled or unlabeled and
subsequently the model queries the subtomogram that have higher probabilities
to be unlabeled. Additionally, HAL introduces a subset sampling strategy to
improve the diversity of the query set, so that the information overlap is
decreased between the queried batches and the algorithmic efficiency is
improved. Our experiments on subtomogram classification tasks using both
simulated and real data demonstrate that we can achieve comparable testing
performance (on average only 3% accuracy drop) by using less than 30% of the
labeled subtomograms, which shows a very promising result for subtomogram
classification task with limited labeling resources.Comment: Statement on authorship changes: Dr. Eric Xing was an academic
advisor of Mr. Haohan Wang. Dr. Xing was not directly involved in this work
and has no direct interaction or collaboration with any other authors on this
work. Therefore, Dr. Xing is removed from the author list according to his
request. Mr. Zhenxi Zhu's affiliation is updated to his current affiliatio