Deep active learning models for imbalanced image classification

Jin, Qiuye; Yuan, Mingzhi; Wang, Haoran; Wang, Manning; Song, Zhijian

doi:10.1016/j.knosys.2022.109817

Cited by 27 publications

(7 citation statements)

References 37 publications

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“…Active Learning has proven to be effective in various applications, including image classification 1 , 3 , 10 , 11 , image retrieval 12 , image captioning 13 , object detection 14 , and regression 15 , 16 . In recent years, Active Learning strategies have been categorized into three main categories: informativeness 16 – 20 , representativeness 8 , 10 , and hybrid approaches 21 , 22 .…”

Section: Related Workmentioning

confidence: 99%

Unlabeled data selection for active learning in image classification

Li,

Wang,

Chen

et al. 2024

Sci Rep

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Active Learning has emerged as a viable solution for addressing the challenge of labeling extensive amounts of data in data-intensive applications such as computer vision and neural machine translation. The main objective of Active Learning is to automatically identify a subset of unlabeled data samples for annotation. This identification process is based on an acquisition function that assesses the value of each sample for model training. In the context of computer vision, image classification is a crucial task that typically requires a substantial training dataset. This research paper introduces innovative selection methods within the Active Learning framework, aiming to identify informative images from unlabeled datasets while minimizing the number of required training data. The proposed methods, namely Similari-ty-based Selection, Prediction Probability-based Selection, and Competence-based Active Learning, have been extensively evaluated through experiments conducted on popular datasets like Cifar10 and Cifar100. The experimental results demonstrate that the proposed methods outperform random selection and conventional selection techniques. The superior performance of the novel selection methods underscores their effectiveness in enhancing the Active Learning process for image classification tasks.

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Section: Related Workmentioning

confidence: 99%

Unlabeled data selection for active learning in image classification

Li,

Wang,

Chen

et al. 2024

Sci Rep

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“…While in the case of a ratio<9, the model must consider the synthetic minority oversampling and undersampling techniques simultaneously for better classification. In [39], [40], deep learning models have been applied to compare the evaluation metrics of osteoarthritis images and imbalances in medical image classification, respectively. Balanced Active Learning (BAL) was proposed by [40] to find the probability of majority and minority class samples in the dataset.…”

Section: Related Workmentioning

confidence: 99%

“…In [39], [40], deep learning models have been applied to compare the evaluation metrics of osteoarthritis images and imbalances in medical image classification, respectively. Balanced Active Learning (BAL) was proposed by [40] to find the probability of majority and minority class samples in the dataset. They performed experiments on imbalanced CIFAR-10, ISIC2020, and Caltech256 datasets.…”

Section: Related Workmentioning

confidence: 99%

A Unified Approach Addressing Class Imbalance in Magnetic Resonance Image for Deep Learning Models

Cui,

Li,

Yang

et al. 2024

IEEE Access

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Medical image datasets, particularly those comprising Magnetic Resonance (MR) images, are essential for accurate diagnosis and treatment planning. However, these datasets often suffer from class imbalance, where certain classes of abnormalities have unequal representation. Models trained on imbalanced datasets can be biased towards the prominent class, leading to misclassification. Addressing class imbalance problems is crucial to developing robust deep-learning MR image analysis models. This research focuses on the class imbalance problem in MR image datasets and proposes a novel approach to enhance deep learning models. We have introduced a unified approach equipped with a selective attention mechanism, unified loss function, and progressive resizing. The selective attention strategy identifies prominent regions within the underlying image to find the feature maps, retaining only the relevant activations of the minority class. Finetuning of the multiple hyperparameters was achieved using a novel unified loss function that plays a vital role in enhancing the overwhelming error performance for minority classes and accuracy for common classes. To address the class imbalances phenomenon, we incorporate progressive resizing that can dynamically adjust the input image size as the model trains. This dynamic nature helps handle class imbalances and improve overall performance. The research evaluates the proposed approach's effectiveness by embedding it into five state-of-the-art CNN models: UNet, FCN, RCNN, SegNet, and Deeplab-V3. For experimental purposes, we have selected five diverse MR image datasets, BUS2017, MICCAI 2015 head and neck, ATLAS, BRATS 2015, and Digital Database Thyroid Image (DDTI), to evaluate the performance of the proposed approach against state-of-the-art techniques. The assessment of the proposed approach reveals improved performance across all metrics for five different MR imaging datasets. DeepLab-V3 demonstrated the best performance, achieving IoU, DSC, Precision, and Recall scores of 0.893, 0.953, 0.943, and 0.944, respectively, on the BUS dataset. These scores indicate an improvement of 5% in DSC, 6% in IoU, 4% in precision, and approximately 4% in recall compared to the baseline. The most significant increases were observed in the ATLAS and LiTS MICCAI 2017 datasets, with a 5% and 7% increase in IoU and DSC over the baseline (DSC = 0.628, DSC = 0.695) for the ATLAS dataset, and a 5% and 9% increase in IoU and DSC for the LiTS MICCAI 2017 dataset.

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“…Active Learning has proven to be effective in various applications, including image classi cation 1,3,10,11 , image retrieval 12 , image captioning 13 , object detection 14 , and regression 15,16 . In recent years, Active Learning strategies have been categorized into three main categories: informativeness 16,17,18,19,20 , representativeness 8,10 , and hybrid approaches 21,22 .…”

Section: Related Work Active Learningmentioning

confidence: 99%

Unlabeled Data Selection for Active Learning in Image Classification

et al. 2023

Preprint

View full text Add to dashboard Cite

Active Learning has emerged as a viable solution for addressing the challenge of labeling extensive amounts of data in data-intensive applications such as computer vision and neural machine translation. The main objective of Active Learning is to automatically identify a subset of unlabeled data samples for annotation. This identification process is based on an acquisition function that assesses the value of each sample for model training. In the context of computer vision, image classification is a crucial task that typically requires a substantial training dataset. This research paper intro-duces innovative selection methods within the Active Learning framework, aiming to identify informative images from unlabeled datasets while minimizing the number of required training data. The proposed methods, namely Similarity-based Selection, Prediction Probability-based Selection, and Competence-based Active Learning, have been extensively evaluated through experiments conducted on popular datasets like Cifar10 and Cifar100. The experimental results demonstrate that the proposed methods outperform random selection and conventional selection techniques. The superior performance of the novel selection methods underscores their effectiveness in enhancing the Active Learning process for image classification tasks.

show abstract

Deep active learning models for imbalanced image classification

Cited by 27 publications

References 37 publications

Unlabeled data selection for active learning in image classification

Unlabeled data selection for active learning in image classification

A Unified Approach Addressing Class Imbalance in Magnetic Resonance Image for Deep Learning Models

Unlabeled Data Selection for Active Learning in Image Classification

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