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RESEARCH PAPER · npj Computational Materials · 2020

Data augmentation in microscopic images for material data mining

Boyuan Ma, Xiaoyan Wei, Chuni Liu, Xiaojuan Ban, Haiyou Huang, Hao Henry Wang, Weihua Xue, Stephen Wu, Mingfei Gao, Qing Shen, Michele Mukeshimana, Adnan Omer Abuassba, Haokai Shen, Yanjing Su

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fabrication12Characterization of microscopic deformation of materials using deep learning algorithms9Visualization of judgment regions in convolutional neural networks for X-ray diffraction and scattering images of aliphatic polyesters11Machine learning assisted empirical formula augmentation21Exploration of the Underlying Space in Microscopic Images via Deep Learning for Additively Manufactured Piezoceramics22Defect recognition in line-space patterns aided by deep learning with data augmentation4Determining the Composition of a Mixed Material with Synthetic Data2A deep learning-based composite design strategy for efficient selection of material and layup sequences from a given database72Center-Guided and Connectivity-Preserving Network for Grain Size Measurement2Continually Reactivating Iterative-Projection Process Method for Instantiating Microstructure from Two-Point Statistics0Image Deep Learning Assisted Prediction of Mechanical and Corrosion Behavior for Al-Zn-Mg Alloys14Machine learning-based image processing in materials science and engineering: A review31A reusable neural network pipeline for unidirectional fiber segmentation11Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net22Artificial intelligence to bring nanomedicine to life189Data-driven modeling of process, structure and property in additive manufacturing: A review and future directions120Accelerated discovery of porous materials for carbon capture by machine learning: A review19Deep learning based object tracking for 3D microstructure reconstruction3A deep learned nanowire segmentation model using synthetic data augmentation43Materials Data toward Machine Learning: Advances and Challenges62Determination of ductile fracture properties of 16MND5 steels under varying constraint levels using machine learning methods53A new cyclical generative adversarial network based data augmentation method for multiaxial fatigue life prediction97Deep learning-based multimodal image analysis for cervical cancer detection54Boundary learning by using weighted propagation in convolution network16Deep learning object detection in materials science: Current state and future directions82Generative Consistency for Semi-Supervised Cerebrovascular Segmentation From TOF-MRA81A Transfer Learning‐Based Method for Facilitating the Prediction of Unsteady Crystal Growth11Continually reactivating iterative-projection method for instantiating microstructure from two-point statistics7Machine learning algorithms in the environmental corrosion evaluation of reinforced concrete structures - A review86Machine learning for high-entropy alloys: Progress, challenges and opportunities397An image recognition based multiaxial low-cycle fatigue life prediction method with CNN model70Generation of microscopic structure of solder material with desirable characteristics based on deep learning5Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118Prediction of mechanical properties for deep drawing steel by deep learning22Advances in artificial intelligence applications in corroded reinforced concrete structures0Clinical Diagnosis of Bone Metastasis in Breast Cancer via a Deep Learning Based Multi-Modal Image Analysis0Synthetic Data Generation for Automatic Segmentation of X-ray Computed Tomography Reconstructions of Complex Microstructures23Multiple-Instance Regression for Metal Powder Hall Flow Rate Prediction Using Augmented Particle Size and Shape Data3Recovering Microscopic Images in Material Science Documents by Image Inpainting3A novel iterative algorithm to improve segmentations with deep convolutional neural networks trained with synthetic X-ray computed tomography data (i.S.Sy.Da.T.A)5All answers are in the images: A review of deep learning for cerebrovascular segmentation37Advances in machine learning- and artificial intelligence-assisted material design of steels109Data quantity governance for machine 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segmentation for microstructure analysis of superalloys via modified U-Net and rectify strategies18Stochastic reconstruction of heterogeneous microstructure combining sliced Wasserstein distance and gradient optimization6Mining experimental data from materials science literature with large language models: an evaluation study27Data-driven approach to predict the fatigue properties of ferrous metal materials using the cGAN and machine-learning algorithms8Deep Learning Methods for Microstructural Image Analysis: The State-of-the-Art and Future Perspectives31Metallurgical Alchemy: Synthesizing Steel Microstructure Images Using DCGANs3Recent progress on machine learning with limited materials data: Using tools from data science and domain knowledge31Instance segmentation from small dataset by a dual-layer semantics-based deep learning framework1Machine learning-assisted design of high-entropy alloys with superior mechanical properties51Digital manufacturing of perovskite materials and 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challenges, and future trajectories33A review on electrolyte innovation enabled by machine learning for energy storage applications5Artificial Intelligence for Materials Discovery, Development, and Optimization171Controllable data augmentation and application of multiaxial fatigue experiments by fatigue conditional generative adversative network model5Skea-Topo: A skeleton-aware loss function for topologically accurate boundary segmentation2Phase diagram determination using machine learning methods in materials science and engineering: a review3Overcoming data scarcity challenges in AI-driven energy chemistry research6Domain-specific simulated data enhances knife-mark noise suppression in microscopy images of materials0Generative learning of morphological and contrast heterogeneities for self-supervised electron micrograph segmentation7Development and Implementation of a Defect Detection Model for Microstructures Using Image Processing Methods0Mixing Real and Synthetic Data in Neural Network Training: A Case Study Regarding Runway Detection0Machine learning for small data sets: an exemplary study on the classification of highly complex surface micromorphologies1Deep learning-driven innovation in metallic materials: A comprehensive review on microstructure analysis, property prediction, and inverse design14Machine learning for materials microstructures: A survey of applications and methodologies5Using the You only Look once Framework for Enhanced Plagioclase Classification in Petrographic Thin Sections0Interpretable convolutional neural network prediction of heat treatment conditions from lithium disilicate glass-ceramic microstructures1Enhanced Multi‐Target Bacterial Detection in Water Samples Using an Optimized Faster R‐CNN Architecture0Focus on latent: Toward a meaningful and interpretable latent representation for diffusion models0AI-Driven Image Processing for Microstructure and Surface Characterization: A Systematic Review of Methods, Materials, and Applications3Machine learning paradigms with multi-strategy data augmentation for predicting anisotropic impact strength of FDM-printed composites0Data-driven design of lightweight alloys: A comprehensive review and a challenge0Improving predictive modeling in materials science based on data augmentation approach0Leveraging large language models to address data scarcity in machine learning for graphene synthesis1AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization2Data as the Backbone of Artificial Intelligence: Insights from Heterogeneous Catalysis0Enhancing strength-ductility synergy in BCC refractory high-entropy alloys via machine learning and bimodal heterostructure design0Machine learning for laser powder bed fusion of Ti-6Al-4V: A review of forming quality, microstructure, and mechanical properties prediction0Towards system-level artificial intelligence in perovskite photovoltaics0

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