Fuzzy Mixture-of-Experts Aggregation for Organoid Identification With Multiscale State Space Features

作者：Xun Deng, Pengwei Hu, Thomas Herget, Feng Tan, Xiaobo Zhu, Jun Zhang, Yu‐An Huang, Lun Hu, Zhu‐Hong You, Xin Luo · 发表于：IEEE Transactions on Fuzzy Systems · 年份：2025 · DOI：10.1109/tfuzz.2025.3622935 · 被引用次数：8 · 研究领域：AI in cancer detection、Cell Image Analysis Techniques、Digital Imaging for Blood Diseases

Accurate and automated identification of organoids from bright-field images is essential for enabling high-throughput drug screening and precision medicine. Organoids, as 3-D in vitro cellular models, closely recapitulate the functional and structural characteristics of their tissue or organ of origin, presenting an unprecedented opportunity for biomedical research. However, the complexity of bright-field microscopy images, including heterogeneous backgrounds and diverse organoid morphologies, poses significant challenges for existing computational methods, often hindering robust feature extraction and high-throughput analysis. To address these issues at the intersection of computational vision and organoid biology, we propose FEMSSorg, a novel organoid recognition framework designed to adaptively aggregate multiscale scan-selected state space features through a fuzzy mixture-of-experts (FuzzyMoE) scoring mechanism. FEMSSorg introduces a fuzzy expert soft routing mechanism (fuzzy route), implemented via Fuzzy C-Means-based soft routing assignments, forming a new class of fuzzy MoE that leverages fuzzy expert clustering scores to dynamically integrate local (LocalSS) and global (GlobalSS) state space features. This approach enables effective balancing of global pixel dependencies and local texture information, thereby substantially reducing background interference and image noise in bright-field images and improving the accuracy of organoid identification. Furthermore, we inco...