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Automated Classification of Inherited Retinal Diseases in Optical Coherence Tomography Images Using Few-shot learning

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Biomedical and Environmental Sciences 2023年第5期36卷 431-440页

作者： ZHAO Qi MAI Si Wei LI Qian HUANG Guan Chong GAO Ming Chen YANG Wen Li WANG Ge MA Ya LI Lei PENG Xiao Yan Department of Ophthalmology Beijing Tongren Eye CenterBeijing Tongren HospitalCapital Medical UniversityBeijing Key Laboratory of Ophthalmology and Visual SciencesBeijing 100730China Department of Computer Science RutgersThe State University of New JerseyNew Brunswick 08901USA Department of Computer Science and Engineering University at BuffaloBuffalo 14260USA Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital Medical UniversityBeijing Ophthalmology and Visual Science KeyLaboratoryBeijing 100730China

Objective To develop a Few-shot learning(FSL) approach for classifying optical coherence tomography(OCT) images in patients with inherited retinal disorders(IRDs).Methods In this study, an FSL model based on a student–teacher learning framework was designed to classify images. 2,317 images from 189 participants were included. Of these, 1,126 images revealed IRDs, 533 were normal samples, and 658 were control samples.Results The FSL model achieved a total accuracy of 0.974–0.983, total sensitivity of 0.934–0.957, total specificity of 0.984–0.990, and total F1 score of 0.935–0.957, which were superior to the total accuracy of the baseline model of 0.943–0.954, total sensitivity of 0.866–0.886, total specificity of 0.962–0.971,and total F1 score of 0.859–0.885. The performance of most subclassifications also exhibited advantages. Moreover, the FSL model had a higher area under curves(AUC) of the receiver operating characteristic(ROC) curves in most subclassifications.Conclusion This study demonstrates the effective use of the FSL model for the classification of OCT images from patients with IRDs, normal, and control participants with a smaller volume of data. The general principle and similar network architectures can also be applied to other retinal diseases with a low prevalence.

关键词： Few-shot learning Student-teacher learning Knowledge distillation Transfer learning Optical coherence tomography Retinal degeneration Inherited retinal diseases

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Sparse spatial transformers for Few-shot learning

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Science China(Information Sciences) 2023年第11期66卷 23-34页

作者： Haoxing CHEN Huaxiong LI Yaohui LI Chunlin CHEN Department of Control Science and Intelligence Engineering Nanjing University

learning from limited data is challenging because data scarcity leads to a poor generalization of the trained model. A classical global pooled representation will probably lose useful local information.Many Few-shot learning methods have recently addressed this challenge using deep descriptors and learning a pixel-level metric. However, using deep descriptors as feature representations may lose image contextual information. Moreover, most of these methods independently address each class in the support set, which cannot sufficiently use discriminative information and task-specific embeddings. In this paper, we propose a novel transformer-based neural network architecture called sparse spatial transformers(SSFormers), which finds task-relevant features and suppresses task-irrelevant features. Particularly, we first divide each input image into several image patches of different sizes to obtain dense local features. These features retain contextual information while expressing local information. Then, a sparse spatial transformer layer is proposed to find spatial correspondence between the query image and the full support set to select task-relevant image patches and suppress task-irrelevant image patches. Finally, we propose using an image patch-matching module to calculate the distance between dense local representations, thus determining which category the query image belongs to in the support set. Extensive experiments on popular Few-shot learning benchmarks demonstrate the superiority of our method over state-of-the-art methods.

关键词： Few-shot learning transformer metric-learning cross-attention

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Meta-BN Net for Few-shot learning

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Frontiers of Computer Science 2023年第1期17卷 73-80页

作者： Wei GAO Mingwen SHAO Jun SHU Xinkai ZHUANG School of Computer Science China University of PetroleumQingdao 266580China School of Mathematics and Statistics Xi’an Jiaotong UniversityXi’an 710049China

In this paper, we propose a lightweight network with an adaptive batch normalization module, called Meta-BN Net, for few-shot classification. Unlike existing Few-shot learning methods, which consist of complex models or algorithms, our approach extends batch normalization, an essential part of current deep neural network training, whose potential has not been fully explored. In particular, a meta-module is introduced to learn to generate more powerful affine transformation parameters, known as and , in the batch normalization layer adaptively so that the representation ability of batch normalization can be activated. The experimental results on miniImageNet demonstrate that Meta-BN Net not only outperforms the baseline methods at a large margin but also is competitive with recent state-of-the-art Few-shot learning methods. We also conduct experiments on fewshot-CIFAR100 and CUB datasets, and the results show that our approach is effective to boost the performance of weak baseline networks. We believe our findings can motivate to explore the undiscovered capacity of base components in a neural network as well as more efficient Few-shot learning methods.

关键词： meta-learning Few-shot learning batch normalization

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Dynamic Analogical Association Algorithm Based on Manifold Matching for Few-shot learning

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Computer Systems Science & Engineering 2023年第7期46卷 1233-1247页

作者： Yuncong Peng Xiaolin Qin Qianlei Wang Boyi Fu Yongxiang Gu Chengdu Institute of Computer Applications Chinese Academy of SciencesChengdu610041China University of Chinese Academy of Sciences Beijing100049China

At present,deep learning has been well applied in many fields.However,due to the high complexity of hypothesis space,numerous training samples are usually required to ensure the reliability of minimizing experience risk.Therefore,training a classifier with a small number of training examples is a challenging task.From a biological point of view,based on the assumption that rich prior knowledge and analogical association should enable human beings to quickly distinguish novel things from a few or even one example,we proposed a dynamic analogical association algorithm to make the model use only a few labeled samples for classification.To be specific,the algorithm search for knowledge structures similar to existing tasks in prior knowledge based on manifold matching,and combine sampling distributions to generate offsets instead of two sample points,thereby ensuring high confidence and significant contribution to the classification.The comparative results on two common benchmark datasets substantiate the superiority of the proposed method compared to existing data generation approaches for Few-shot learning,and the effectiveness of the algorithm has been proved through ablation experiments.

关键词： Few-shot learning manifold matching analogical association data generation

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A New Diagnosis Method with Few-shot learning Based on a Class-rebalance Strategy for Scarce Faults in Industrial Processes

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Machine Intelligence Research 2023年第4期20卷 583-594页

作者： Xinyao Xu De Xu Fangbo Qin Research Center of Precision Sensing and Control Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing100049China

For industrial processes, new scarce faults are usually judged by experts. The lack of instances for these faults causes a severe data imbalance problem for a diagnosis model and leads to low performance. In this article, a new diagnosis method with Few-shot learning based on a class-rebalance strategy is proposed to handle the problem. The proposed method is designed to transform instances of the different faults into a feature embedding space. In this way, the fault features can be transformed into separate feature clusters. The fault representations are calculated as the centers of feature clusters. The representations of new faults can also be effectively calculated with few support instances. Therefore, fault diagnosis can be achieved by estimating feature similarity between instances and faults. A cluster loss function is designed to enhance the feature clustering performance. Also, a class-rebalance strategy with data augmentation is designed to imitate potential faults with different reasons and degrees of severity to improve the model’s generalizability. It improves the diagnosis performance of the proposed method. Simulations of fault diagnosis with the proposed method were performed on the Tennessee-Eastman benchmark. The proposed method achieved average diagnosis accuracies ranging from 81.8% to 94.7% for the eight selected faults for the simulation settings of support instances ranging from 3 to 50. The simulation results verify the effectiveness of the proposed method.

关键词： Data augmentation feature clustering class-rebalance strategy Few-shot learning fault diagnosis

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Recent advances of Few-shot learning methods and applications

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Science China(Technological Sciences) 2023年第4期66卷 920-944页

作者： WANG JianYuan LIU KeXin ZHANG YuCheng LENG Biao LU JinHu School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China

The rapid development of deep learning provides great convenience for production and life.However,the massive labels required for training models limits further development.Few-shot learning which can obtain a high-performance model by learning few samples in new tasks,providing a solution for many scenarios that lack samples.This paper summarizes Few-shot learning algorithms in recent years and proposes a taxonomy.Firstly,we introduce the Few-shot learning task and its significance.Secondly,according to different implementation strategies,Few-shot learning methods in recent years are divided into five categories,including data augmentation-based methods,metric learning-based methods,parameter optimization-based methods,external memory-based methods,and other approaches.Next,We investigate the application of Few-shot learning methods and summarize them from three directions,including computer vision,human-machine language interaction,and robot actions.Finally,we analyze the existing Few-shot learning methods by comparing evaluation results on mini Image Net,and summarize the whole paper.

关键词： Few-shot learning deep learning meta learning data augmentation parameter optimization

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Cross Modal Adaptive Few-shot learning Based on Task Dependence

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Chinese Journal of Electronics 2023年第1期32卷 85-96页

作者： DAI Leichao FENG Lin SHANG Xinglin SU Han School of Computer Science Sichuan Normal University

Few-shot learning(FSL) is a new machine learning method that applies the prior knowledge from some different domains tasks. The existing FSL models of metric-based learning have some drawbacks,such as the extracted features cannot reflect the true data distribution and the generalization ability is weak. In order to solve the problem in the present, we developed a model named cross modal adaptive Few-shot learning based on task dependence(COOPERATE for short). A feature extraction and task representation method based on task condition network and auxiliary co-training is proposed. Semantic representation is added to each task by combining both visual and textual features. The measurement scale is adjusted to change the property of parameter update of the algorithm. The experimental results show that the COOPERATE has the better performance comparing with all approaches of the monomode and modal alignment FSL.

关键词： Meta-learning Few-shot learning Metric learning Cross modal

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Few-shot learning for biotic stress classification of coffee leaves

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Artificial Intelligence in Agriculture 2022年第1期6卷 55-67页

作者： Lucas M.Tassis Renato A.Krohling LABCIN-Nature Inspired Computing Lab Federal University of Espirito SantoAv.Fernando Ferrari514CEP29075-910 VitóriaEspırito SantoESBrazil PPGI-Graduate Program in Computer Science Federal University of Espirito SantoAv.Fernando Ferrari514CEP 29075-910VitóriaEspírito SantoESBrazil

In the last few years,deep neural networks have achieved promising results in several fields.However,one of the main limitations of these methods is the need for large-scale datasets to properly generalize.Few-shot learning methods emerged as an attempt to solve this shortcoming.Among the Few-shot learning methods,there is a class of methods known as embedding learning or metric learning.These methods tackle the classification problem by learning to compare,needing fewer training data.One of the main problems in plant diseases and pests recognition is the lack of large public datasets available.Due to this difficulty,the field emerges as an intriguing application to evaluate the Few-shot learning methods.The field is also relevant due to the social and economic importance of agriculture in several countries.In this work,datasets consisting of biotic stresses in coffee leaves are used as a case study to evaluate the performance of Few-shot learning in classification and severity estimation tasks.We achieved competitive results compared with the ones reported in the literature in the classification task,with accuracy values close to 96%.Furthermore,we achieved superior results in the severity estimation task,obtaining 6.74%greater accuracy than the baseline.

关键词： Plant diseases and pests classification Image classification Few-shot learning Meta-learning Convolutional neural networks

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Ensemble Making Few-shot learning Stronger

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Data Intelligence 2022年第3期4卷 529-551页

作者： Qiang Lin Yongbin Liu Wen Wen Zhihua Tao Chunping Ouyang Yaping Wan Computer School University of South China 421001China Hunan provincial base for scientific and technological innovation cooperation HunanChina

Few-shot learning has been proposed and rapidly emerging as a viable means for completing various tasks.Many few-shot models have been widely used for relation learning tasks.However,each of these models has a shortage of capturing a certain aspect of semantic features,for example,CNN on long-range dependencies part,Transformer on local features.It is difficult for a single model to adapt to various relation learning,which results in a high variance problem.Ensemble strategy could be competitive in improving the accuracy of few-shot relation extraction and mitigating high variance risks.This paper explores an ensemble approach to reduce the variance and introduces fine-tuning and feature attention strategies to calibrate relation-level features.Results on several few-shot relation learning tasks show that our model significantly outperforms the previous state-of-the-art models.

关键词： Few-shot learning Relation extraction Ensemble learning Attention Mechanism Fine-tuning

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SW-Net: A novel Few-shot learning approach for disease subtype prediction

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BIOCELL 2023年第3期47卷 569-579页

作者： YUHAN JI YONG LIANG ZIYI YANG NING AI Faculty of Innovation Engineering School of Computer Science and EngineeringMacao University of Science and TechnologyMacao999078China Tencent Quantum Lab Shenzhen518000China

Few-shot learning is becoming more and more popular in many fields,especially in the computer vision field.This inspires us to introduce Few-shot learning to the genomic field,which faces a typical few-shot problem because some tasks only have a limited number of samples with high-dimensions.The goal of this study was to investigate the few-shot disease sub-type prediction problem and identify patient subgroups through training on small data.Accurate disease subtype classification allows clinicians to efficiently deliver investigations and interventions in clinical practice.We propose the SW-Net,which simulates the clinical process of extracting the shared knowledge from a range of interrelated tasks and generalizes it to unseen data.Our model is built upon a simple baseline,and we modified it for genomic data.Supportbased initialization for the classifier and transductive fine-tuning techniques were applied in our model to improve prediction accuracy,and an Entropy regularization term on the query set was appended to reduce over-fitting.Moreover,to address the high dimension and high noise issue,we future extended a feature selection module to adaptively select important features and a sample weighting module to prioritize high-confidence samples.Experiments on simulated data and The Cancer Genome Atlas meta-dataset show that our new baseline model gets higher prediction accuracy compared to other competing algorithms.

关键词： Few-shot learning Disease sub-type classification Feature selection Deep learning Meta-learning

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