A noise-robust classification method for cryo-ET subtomograms with out-of-distribution detection.

Abstract

Cryogenic electron tomography (cryo-ET) enables high-resolution 3D reconstruction of biological samples, with accurate subtomogram classification critical for structural analysis. However, current subtomogram classification methods often struggle with out-of-distribution (OOD) data issue, causing misclassification and mismatched structures. To solve this problem, we propose a unified subtomogram classification framework that incorporates OOD detection to distinguish unknown (OOD) from known (in-distribution, ID) classes and predict labels for ID data, thereby enhancing existing subtomogram classification methods. Within this framework, we develop a noise-robust classification method that integrates a 3D discrete wavelet transform-based encoder to reduce high-frequency noise and extract robust features. Additionally, we incorporate a Mahalanobis distance-based OOD detector with a reliable metric for 3D subtomograms and introduce an adaptive classifier that adjusts to accommodate datasets of varying scales. The experimental and visualization results demonstrate that our noise-robust method improves subtomogram classification accuracy and effectively models features while enhancing OOD detection. Our code is available at https://github.com/yxs1137/Subtomo-Classification-with-OOD.git. The real data used in this study can be accessed through CryoET Data Portal.