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Enhancing 3D Reconstruction Accuracy of FIB Tomography Data Using Multi‑voltage Images and multimodal machine learning

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Nanomanufacturing and Metrology 2024年第1期7卷 48-60页

作者： Trushal Sardhara Alexander Shkurmanov Yong Li Lukas Riedel Shan Shi Christian J.Cyron Roland C.Aydin Martin Ritter Institute for Continuum and Material Mechanics Hamburg University of TechnologyHamburgGermany Electron Microscopy Unit Hamburg University of TechnologyHamburgGermany Institute of Materials Physics and Technology Hamburg University of TechnologyHamburgGermany Institute of Materials Mechanics Helmholtz-Zentrum HereonGeesthachtGermany Research Group of Integrated Metallic Nanomaterials Systems Hamburg University of TechnologyHamburgGermany Institute of Material Systems Modeling Helmholtz-Zentrum HereonGeesthachtGermany

FIB-SEM tomography is a powerful technique that integrates a focused ion beam(FIB)and a scanning electron microscope(SEM)to capture high-resolution imaging data of nanostructures.This approach involves collecting in-plane SEM imagesand using FIB to remove material layers for imaging subsequent planes,thereby producing image stacks.However,theseimage stacks in FIB-SEM tomography are subject to the shine-through effect,which makes structures visible from theposterior regions of the current plane.This artifact introduces an ambiguity between image intensity and structures in thecurrent plane,making conventional segmentation methods such as thresholding or the k-means algorithm insufficient.Inthis study,we propose a multimodal machine learning approach that combines intensity information obtained at differentelectron beam accelerating voltages to improve the three-dimensional(3D)reconstruction of nanostructures.By treatingthe increased shine-through effect at higher accelerating voltages as a form of additional information,the proposed methodsignificantly improves segmentation accuracy and leads to more precise 3D reconstructions for real FIB tomography data.

关键词： multimodal machine learning Multi-voltage images FIB-SEM Overdeterministic systems 3D reconstruction FIB tomography

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Label distribution for multimodal machine learning

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Frontiers of Computer Science 2022年第1期16卷 33-43页

作者： Yi REN Ning XU Miaogen LING Xin GENG Department of Computer Science and Engineering Southeast UniversityNanjing211189China

multimodal machine learning(MML)aims to understand the world from multiple related modalities.It has attracted much attention as multimodal data has become increasingly available in real-world application.It is shown that MML can perform better than single-modal machine learning,since multi-modalities containing more information which could complement each other.However,it is a key challenge to fuse the multi-modalities in MML.Different from previous work,we further consider the side-information,which reflects the situation and influences the fusion of multi-modalities.We recover multimodal label distribution(MLD)by leveraging the side-information,representing the degree to which each modality contributes to describing the instance.Accordingly,a novel framework named multimodal label distribution learning(MLDL)is proposed to recover the MLD,and fuse the multimodalities with its guidance to learn an in-depth understanding of the jointly feature representation.Moreover,two versions of MLDL are proposed to deal with the sequential data.Experiments on multimodal sentiment analysis and disease prediction show that the proposed approaches perform favorably against state-of-the-art methods.

关键词： multimodal machine learning label distribution learning sentiment analysis disease prediction

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