A novel transfer learning for recognition of overlapping nano object

Han, Yuexing; Liu, Yuhong; Wang, Bing; Chen, Qiaochuan; Leilei, Song; Lin, Tong; Lai, Chuanbin; Konagaya, Akihiko

doi:10.1007/s00521-021-06731-y

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…Analysis of these data streams necessitates rapid classication and identication of the observed objects. An oen-encountered scenario is the one in which the individual objects are separable, corresponding to the strong dilution of original solution, [23][24][25][26] rare defects, [27][28][29][30] or easily identiable borders of the objects. [31][32][33][34][35] In these cases, the compound images containing multiple objects can be separated into the patches containing individual objects of interest, albeit at arbitrary orientation, with positional jitter relative to the center of the patch due to the variability of object shapes.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle libraries

Ziatdinov,

Yaman,

Liu

et al. 2024

Digital Discovery

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Section: Introductionmentioning

confidence: 99%

Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle libraries

Ziatdinov,

Yaman,

Liu

et al. 2024

Digital Discovery

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“…[4][5][6][7][8] Recent advances in deep learning have led to a surge of applications in electron microscopy image analysis for a diverse set of tasks in two main categories: discriminative and generative. Discriminative tasks are tasks like morphology/phase classication, [9][10][11][12] particle/defect detection, [13][14][15][16] image quality assessment, [17][18][19] and segmentation [20][21][22][23][24][25] where the objective is quantied by how well the model can distinguish (1) between images or (2) between objects and their background. Generative tasks include microstructure reconstruction, [26][27][28] super resolution, [29][30][31] autofocus 32 and denoising 33,34 where the objective is generation of images with certain desired traits.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised machine learning workflow for analysis of nanowire morphologies from transmission electron microscopy images

Montz

Emrick

et al. 2022

Digital Discovery

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“…Parameter hyper-tuning [19] in the selected model is the process of ascertaining some particular parameters to optimize the performance of learning algorithms on a specific set. Parameter tuning has proven its vital role in improving the accuracy and overall model performance both for ensemble and non-ensemble classifiers [20,21]. Every classifier has its own set of parameters and needs to tune following the different tuning steps by performing an exhaustive grid search.…”

mentioning

confidence: 99%

Generation of Controlled Synthetic Samples and Impact of Hyper-Tuning Parameters to Effectively Classify the Complex Structure of Overlapping Region

Mahmood

Butt

Rehman³

et al. 2022

Applied Sciences

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The classification of imbalanced and overlapping data has provided customary insight over the last decade, as most real-world applications comprise multiple classes with an imbalanced distribution of samples. Samples from different classes overlap near class boundaries, creating a complex structure for the underlying classifier. Due to the imbalanced distribution of samples, the underlying classifier favors samples from the majority class and ignores samples representing the least minority class. The imbalanced nature of the data—resulting in overlapping regions—greatly affects the learning of various machine learning classifiers, as most machine learning classifiers are designed to handle balanced datasets and perform poorly when applied to imbalanced data. To improve learning on multi-class problems, more expertise is required in both traditional classifiers and problem domain datasets. Some experimentation and knowledge of hyper-tuning the parameters and parameters of the classifier under consideration are required. Several techniques for learning from multi-class problems have been reported in the literature, such as sampling techniques, algorithm adaptation methods, transformation methods, hybrid methods, and ensemble techniques. In the current research work, we first analyzed the learning behavior of state-of-the-art ensemble and non-ensemble classifiers on imbalanced and overlapping multi-class data. After analysis, we used grid search techniques to optimize key parameters (by hyper-tuning) of ensemble and non-ensemble classifiers to determine the optimal set of parameters to enhance the learning from a multi-class imbalanced classification problem, performed on 15 public datasets. After hyper-tuning, 20% of the dataset samples are synthetically generated to add to the majority class of each respective dataset to make it more overlapped (complex structure). After the synthetic sample’s addition, the hyper-tuned ensemble and non-ensemble classifiers are tested over that complex structure. This paper also includes a brief description of tuned parameters and their effects on imbalanced data, followed by a detailed comparison of ensemble and non-ensemble classifiers with the default and tuned parameters for both original and synthetically overlapped datasets. We believe that the underlying paper is the first kind of effort in this domain, which will furnish various research aspects to with a greater focus on the parameters of the classifier in the field of learning from imbalanced data problems using machine-learning algorithms.

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A novel transfer learning for recognition of overlapping nano object

Cited by 5 publications

References 31 publications

Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle libraries

Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle libraries

Semi-supervised machine learning workflow for analysis of nanowire morphologies from transmission electron microscopy images

Generation of Controlled Synthetic Samples and Impact of Hyper-Tuning Parameters to Effectively Classify the Complex Structure of Overlapping Region

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