Szu-Chi Chung

Szu-Chi Chung

In single-particle cryo-electron microscopy (cryo-EM), the efficient determination of orientation parameters for 2D projection images poses a significant challenge yet is crucial for reconstructing 3D structures. This task is complicated by the high noise levels present in the cryo-EM datasets, which often include outliers, necessitating several time-consuming 2D clean-up processes. Recently, solutions based on deep learning have emerged, offering a more streamlined approach to the traditionally laborious task of orientation estimation. These solutions often employ amortized inference, eliminating the need to estimate parameters individually for each image. However, these methods frequently overlook the presence of outliers and may not adequately concentrate on the components used within the network. This paper introduces a novel approach that uses a 10-dimensional feature vector to represent the orientation and applies a Quadratically-Constrained Quadratic Program to derive the predicted orientation as a unit quaternion, supplemented by an uncertainty metric. Furthermore, we propose a unique loss function that considers the pairwise distances between orientations, thereby enhancing the accuracy of our method. Finally, we also comprehensively evaluate the design choices involved in constructing the encoder network, a topic that has not received sufficient attention in the literature. Our numerical analysis demonstrates that our methodology effectively recovers orientations from 2D cryo-EM images in an end-to-end manner. Importantly, the inclusion of uncertainty quantification allows for direct clean-up of the dataset at the 3D level. Lastly, we package our proposed methods into a user-friendly software suite named cryo-forum, designed for easy accessibility by the developers.

Pei-Han Chen, Szu-Chi Chung

In machine learning, research has traditionally focused on model development, with relatively less attention paid to training data. As model architectures have matured and marginal gains from further refinements diminish, data quality has emerged as a critical factor. However, systematic studies on evaluating and ensuring dataset quality in the image domain remain limited. This study investigates methods for systematically assessing image dataset quality and examines how various image quality factors influence model performance. Using the publicly available and relatively clean CIFAKE dataset, we identify common quality issues and quantify their impact on training. Building on these findings, we develop a pipeline that integrates two community-developed tools, CleanVision and Fastdup. We analyze their underlying mechanisms and introduce several enhancements, including automatic threshold selection to detect problematic images without manual tuning. Experimental results demonstrate that not all quality issues exert the same level of impact. While convolutional neural networks show resilience to certain distortions, they are particularly vulnerable to degradations that obscure critical visual features, such as blurring and severe downscaling. To assess the performance of existing tools and the effectiveness of our proposed enhancements, we formulate the detection of low-quality images as a binary classification task and use the F1 score as the evaluation metric. Our automatic thresholding method improves the F1 score from 0.6794 to 0.9468 under single perturbations and from 0.7447 to 0.8557 under dual perturbations. For near-duplicate detection, our deduplication strategy increases the F1 score from 0.4576 to 0.7928. These results underscore the effectiveness of our workflow and provide a foundation for advancing data quality assessment in image-based machine learning.

Szu-Chi Chung, Po-Cheng Chou

Differentiating signals from the background in micrographs is a critical initial step for cryogenic electron microscopy (cryo-EM), yet it remains laborious due to low signal-to-noise ratio (SNR), the presence of contaminants and densely packed particles of varying sizes. Although image segmentation has recently been introduced to distinguish particles at the pixel level, the low SNR complicates the automated generation of accurate annotations for training supervised models. Moreover, platforms for systematically comparing different design choices in pipeline construction are lacking. Thus, a modular framework is essential to understand the advantages and limitations of this approach and drive further development. To address these challenges, we present a pipeline that automatically generates high-quality segmentation maps from cryo-EM data to serve as ground truth labels. Our modular framework enables the selection of various segmentation models and loss functions. We also integrate Conditional Random Fields (CRFs) with different solvers and feature sets to refine coarse predictions, thereby producing fine-grained segmentation. This flexibility facilitates optimal configurations tailored to cryo-EM datasets. When trained on a limited set of micrographs, our approach achieves over 90% accuracy, recall, precision, Intersection over Union (IoU), and F1-score on synthetic data. Furthermore, to demonstrate our framework's efficacy in downstream analyses, we show that the particles extracted by our pipeline produce 3D density maps with higher resolution than those generated by existing particle pickers on real experimental datasets, while achieving performance comparable to that of manually curated datasets from experts.

Szu-Chi Chung, Shao-Hsuan Wang, Po-Yao Niu et al.

Principal component analysis (PCA) is arguably the most widely used dimension-reduction method for vector-type data. When applied to a sample of images, PCA requires vectorization of the image data, which in turn entails solving an eigenvalue problem for the sample covariance matrix. We propose herein a two-stage dimension reduction (2SDR) method for image reconstruction from high-dimensional noisy image data. The first stage treats the image as a matrix, which is a tensor of order 2, and uses multilinear principal component analysis (MPCA) for matrix rank reduction and image denoising. The second stage vectorizes the reduced-rank matrix and achieves further dimension and noise reduction. Simulation studies demonstrate excellent performance of 2SDR, for which we also develop an asymptotic theory that establishes consistency of its rank selection. Applications to cryo-EM (cryogenic electronic microscopy), which has revolutionized structural biology, organic and medical chemistry, cellular and molecular physiology in the past decade, are also provided and illustrated with benchmark cryo-EM datasets. Connections to other contemporaneous developments in image reconstruction and high-dimensional statistical inference are also discussed.