Medical image data augmentation: techniques, comparisons and interpretations

Göçeri, Evgin

doi:10.1007/s10462-023-10453-z

Cited by 113 publications

(29 citation statements)

References 123 publications

(105 reference statements)

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“…The data utilized in this study comprised urinary tract stones, which were expanded through various data augmentation techniques [ 12 ]. These methods have been employed to enhance the model’s performance in processing diverse urinary images in real-world settings.…”

Section: Methodsmentioning

confidence: 99%

Transfer Learning for Effective Urolithiasis Detection

et al. 2023

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Purpose: Urolithiasis is a common disease that can cause acute pain and complications. The objective of this study was to develop a deep learning model utilizing transfer learning for the rapid and accurate detection of urinary tract stones. By employing this method, we aim to improve the efficiency of medical staff and contribute to the progress of deep learning-based medical image diagnostic technology.Methods: The ResNet50 model was employed to develop feature extractors for detecting urinary tract stones. Transfer learning was applied by utilizing the weights of pretrained models as initial values, and the models were fine-tuned with the provided data. The model’s performance was evaluated using accuracy, precision-recall, and receiver operating characteristic curve metrics.Results: The ResNet-50-based deep learning model demonstrated high accuracy and sensitivity, outperforming traditional methods. Specifically, it enabled a rapid diagnosis of the presence or absence of urinary tract stones, thereby assisting doctors in their decision-making process.Conclusions: This research makes a meaningful contribution by accelerating the clinical implementation of urinary tract stone detection technology utilizing ResNet-50. The deep learning model can swiftly identify the presence or absence of urinary tract stones, thereby enhancing the efficiency of medical staff. We expect that this study will contribute to the advancement of medical imaging diagnostic technology based on deep learning.

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Section: Methodsmentioning

confidence: 99%

Transfer Learning for Effective Urolithiasis Detection

et al. 2023

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“…In addressing the unique challenges posed by the small and amorphous morphological features of moss, traditional image augmentation methods often fall short, necessitating a cautious approach to the selection of augmentation algorithms. These algorithms must be carefully matched to the specific characteristics of the images and the classification task at hand, as highlighted in references [29] and [30]. When the original moss images are fed into Swin-R, they are resized to 224×224 pixels (as shown in Table 2).…”

Section: Methods a Data Preprocessingmentioning

confidence: 99%

Swin Routiformer: Moss Classification Algorithm Based on Swin Transformer With Bi-Level Routing Attention

Li,

Wang,

Wang

et al. 2024

IEEE Access

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Accurate classification of moss species is essential for progress in ecology and biology. However, traditional methods for classifying moss require significant expertise, and current deep learning techniques struggle due to limited dataset diversity and poor performance in multi-class classification tasks. To overcome these challenges, we proposed the Swin Routiformer, a new algorithm for moss image classification that enhances the Swin Transformer with bi-level routing attention. Addressing the issue of limited data, we constructed a dataset with images of 110 different moss types. Additionally, we propose the Crop-Similar data augmentation algorithm, specifically designed for moss images, to reduce background noise interference and prevent information loss due to feature scaling. Adopting the Swin Transformer model with its multi-level hierarchical architecture for visual feature extraction, we introduce the Swin Routiformer Block, which enhances the network's feature interaction capabilities, reduces computational complexity, and improves classification accuracy and image processing speed for moss species. Our experimental results show that the Swin Routiformer achieves a top-1 accuracy of 82.19% and an f1-score of 82.79% on the test set, outperforming most mainstream models by 4.53% and 1.81% respectively compared to the baseline Swin Transformer model. These findings establish the Swin Routiformer as a valuable tool for the precise identification of moss species, offering significant contributions to the related fields.

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“…As a potential future endeavour, a comparison between the performance of the proposed method and segmentation methods based on capsule networks could be explored, delving into their respective advantages. Lastly, it is well recognized that deep networks require ample data and various augmentation techniques have been applied to address issues and enhance the reliability and robustness of deep networks [49–51]. Therefore, as an extension of this work, evaluating the proposed method with an increased amount of data could be considered.…”

Section: Conculsion and Future Workmentioning

confidence: 99%

Brain tumour segmentation framework with deep nuanced reasoning and Swin‐T

Xu,

Yu,

et al. 2024

IET Image Processing

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Tumour medical image segmentation plays a crucial role in clinical imaging diagnosis. Existing research has achieved good results, enabling the segmentation of three tumour regions in MRI brain tumour images. Existing models have limited focus on the brain tumour areas, and the long‐term dependency of features is weakened as the network depth increases, resulting in blurred edge segmentation of the targets. Additionally, considering the excellent segmentation performance of the Swin Transformer(Swin‐T) network, its network structure and parameters are relatively large. To address these limitations, this paper proposes a brain tumour segmentation framework with deep nuanced reasoning and Swin‐T. It is mainly composed of the backbone hybrid network (BHN) and the deep micro texture extraction module (DMTE). The BHN combines the Swin‐T stage with a new downsampling transition module called dual path feature reasoning (DPFR). The entire network framework is designed to extract global and local features from multi‐modal data, enabling it to capture and analyze deep texture features in multi‐modal images. It provides significant optimization over the Swin‐T network structure. Experimental results on the BraTS dataset demonstrate that the proposed method outperforms other state‐of‐the‐art models in terms of segmentation performance. The corresponding source codes are available at https://github.com/CurbUni/Brain‐Tumor‐Segmentation‐Framework‐with‐Deep‐Nuanced‐Reasoning‐and‐Swin‐T.

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Medical image data augmentation: techniques, comparisons and interpretations

Cited by 113 publications

References 123 publications

Transfer Learning for Effective Urolithiasis Detection

Transfer Learning for Effective Urolithiasis Detection

Swin Routiformer: Moss Classification Algorithm Based on Swin Transformer With Bi-Level Routing Attention

Brain tumour segmentation framework with deep nuanced reasoning and Swin‐T

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