An Enhanced Framework of Generative Adversarial Networks (EF-GANs) for Environmental Microorganism Image Augmentation With Limited Rotation-Invariant Training Data

Xu, Hao; Li, Chen; Rahaman, Md Mamunur; Yao, Yu-Dong; Jing-hua, Zhang; Kulwa, Frank; Zhao, Xin; Qi, Shouliang; Teng, Yueyang

doi:10.1109/access.2020.3031059

Cited by 28 publications

(24 citation statements)

References 37 publications

(33 reference statements)

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“…As a further improvement of this study, it could consider other data-augmentation techniques using also other geometric transformations (e.g., cropping, rotation, stretching) and histogram-based operations. Moreover, Generative Adversarial Network (GAN)-based data augmentation might be investigated, in terms of both image enhancement [59] and geometrical transformations [60], for classification and detection tasks [61]. The preparation of adversarial examples-by applying small modifications to the original images that are close to the decision boundaries learned by a classifier [62]-might affect Deep Learning based fingerprint recognition systems [63] and authentication [64].…”

Section: Discussionmentioning

confidence: 99%

Fingerprint Classification Based on Deep Learning Approaches: Experimental Findings and Comparisons

et al. 2021

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Biometric classification plays a key role in fingerprint characterization, especially in the identification process. In fact, reducing the number of comparisons in biometric recognition systems is essential when dealing with large-scale databases. The classification of fingerprints aims to achieve this target by splitting fingerprints into different categories. The general approach of fingerprint classification requires pre-processing techniques that are usually computationally expensive. Deep Learning is emerging as the leading field that has been successfully applied to many areas, such as image processing. This work shows the performance of pre-trained Convolutional Neural Networks (CNNs), tested on two fingerprint databases—namely, PolyU and NIST—and comparisons to other results presented in the literature in order to establish the type of classification that allows us to obtain the best performance in terms of precision and model efficiency, among approaches under examination, namely: AlexNet, GoogLeNet, and ResNet. We present the first study that extensively compares the most used CNN architectures by classifying the fingerprints into four, five, and eight classes. From the experimental results, the best performance was obtained in the classification of the PolyU database by all the tested CNN architectures due to the higher quality of its samples. To confirm the reliability of our study and the results obtained, a statistical analysis based on the McNemar test was performed.

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Section: Discussionmentioning

confidence: 99%

Fingerprint Classification Based on Deep Learning Approaches: Experimental Findings and Comparisons

et al. 2021

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“…The data enhancement techniques can apply to other areas, such as database enhancements in the field of microbiology. In [ 87 ], the microbial data are expanded by combining geometric transformation and GAN network. In the pathological images of breast cancer, the number of datasets is also insufficient.…”

Section: Methods Analysismentioning

confidence: 99%

A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development

et al. 2021

BioMed Research International

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Gastric cancer is a common and deadly cancer in the world. The gold standard for the detection of gastric cancer is the histological examination by pathologists, where Gastric Histopathological Image Analysis (GHIA) contributes significant diagnostic information. The histopathological images of gastric cancer contain sufficient characterization information, which plays a crucial role in the diagnosis and treatment of gastric cancer. In order to improve the accuracy and objectivity of GHIA, Computer-Aided Diagnosis (CAD) has been widely used in histological image analysis of gastric cancer. In this review, the CAD technique on pathological images of gastric cancer is summarized. Firstly, the paper summarizes the image preprocessing methods, then introduces the methods of feature extraction, and then generalizes the existing segmentation and classification techniques. Finally, these techniques are systematically introduced and analyzed for the convenience of future researchers.

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“…Previous work [1] has proposed the procedure of few-shot setup. We have three datasets: train dataset ( ), validation dataset ( ), test dataset ( ).…”

Section: Few-shot Setupmentioning

confidence: 99%

“…Thus, the class with more samples may dominate the taskspecific learning in the training stage, and the fewer shots classes have poor representation. (Class A has 500 samples; B class has ten samples) 2) Class chosen imbalance: In previous work [1], this model has learned that in each episode, the model randomly chose n-way-k-shot to construct tasks, which leads to some classes be chosen more times but others are fewer. When our Dataset is extensive, and there are many episodes, this imbalance will become more serious.…”

Section: Learn To Balancementioning

confidence: 99%

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Hierarchically Structured Network With Attention Convolution and Embedding Propagation for Imbalanced Few-Shot Learning

et al. 2021

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Generally, a few-shot distribution shift will lead to a poor generalization. Furthermore, while the number of instances of each class in the real world may significantly different, the existing few-shot classification methods are based on the assumption that the number of samples in each class is equal, which causes the trained classifier invalid. Moreover, through ResNet and WRN (Wide Residual Network) have achieved great success in the image processing field, the depth and width of CNNs constrain the conventional convolution layer performance. Thus, to overcome the above problems, the model of this paper proposes a novel few-shot classification model that uses learning balance variables to decide how much to learn from the imbalance dataset, which dynamically generates the convolution kernel based on each input. In our model, to extend the decision boundaries and enhance the class representations, this paper uses embedding propagation as a regularizer for manifold smoothing. Manifold smoothing can effectively solve the above problems of transductive learning. The interpolations between neural network features based on similarity graphs are used by embedding propagation. Experiments show that embedding propagation can produce a better embedding manifold and our model in standard few-shot datasets, such as miniImagenet, tieredImagenet, CUB has state-of-the-art results. It significantly outperforms the existing few-shot approaches, which consistently improves the accuracy of the models by about 11%.

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An Enhanced Framework of Generative Adversarial Networks (EF-GANs) for Environmental Microorganism Image Augmentation With Limited Rotation-Invariant Training Data

Cited by 28 publications

References 37 publications

Fingerprint Classification Based on Deep Learning Approaches: Experimental Findings and Comparisons

Fingerprint Classification Based on Deep Learning Approaches: Experimental Findings and Comparisons

A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development

Hierarchically Structured Network With Attention Convolution and Embedding Propagation for Imbalanced Few-Shot Learning

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