Synthetic data adaptation improves few-shot cryo-ET classification accuracy
Bridging the Synthetic-to-Real Gap for Few-Shot Cryo-ET Classification
Computer Vision and Pattern RecognitionMachine Learning
Summary
Classifying tiny 3D images from cryo-electron tomography is hard because there aren’t many labeled examples to learn from. The authors show that while computer-generated synthetic images could help, they usually look different from real ones, causing mistakes. They created a new method that transforms synthetic data to look more like real data and adjusts features to close this gap. This approach helps computers learn better from very few real examples and improves classification performance.
What this means in practice
- •For biotech imaging teams: Improve classification of cryo-ET images when only a few labeled samples are available by using synthetic data adapted to real conditions.
- •For medical image analysts: Enhance 3D microscopic image classification performance by bridging gaps between simulated and actual data in few-shot contexts.
Authors
Siddhant Bharadwaj, Ashish Vashist, Rashi Singh, Pranav Vinodh, Nishanth Artham, Runmin Jiang, Xingjian Li, Min Xu
Abstract
Subtomogram classification in cryo-electron tomography (cryo-ET) is a challenging problem due to the scarcity of labeled examples. While cryo-ET simulators can be adopted to generate unlimited synthetic data, the substantial domain gap between synthetic and real subtomograms hinders its practical utilization. In this work, we propose a novel synthetic-to-real adaptation framework with a learnable transformation module, bridging this gap at both the input and feature levels. Extensive experiments demonstrate that our method consistently outperforms existing transfer learning baselines in few-shot settings.