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1篇 您的检索式：作者名="Joshua Stuckner"

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		题名	作者	年代	出处	被引量
	1	Microstructure segmentation with deep learning encoders pre-trained on a large microscopy dataset显示文摘This study examined the improvement of microscopy segmentation intersection over union accuracy by transfer learning from a large dataset of microscopy images called MicroNet.Many neural network encoder architectures were trained on over 100,000 labeled microscopy images from 54 material classes.These pre-trained encoders were then embedded into multiple segmentation architectures including UNet and DeepLabV3+to evaluate segmentation performance on created benchmark microscopy datasets.Compared to ImageNet pre-training,models pre-trained on MicroNet generalized better to out-of-distribution micrographs taken under different imaging and sample conditions and were more accurate with less training data.When training with only a single Ni-superalloy image,pre-training on MicroNet produced a 72.2%reduction in relative intersection over union error.These results suggest that transfer learning from large in-domain datasets generate models with learned feature representations that are more useful for downstream tasks and will likely improve any microscopy image analysis technique that can leverage pre-trained encoders.	Joshua Stuckner Bryan Harder Timothy M.Smith	2022	npj Computational Materials2022，，1：	2

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