The Effect of Data Augmentation in Deep Learning Approach for Peripheral Blood Leukocyte Recognition

Nozaka, Hiroyuki; M, Oda; Sasaki, Ami; Harako, Honoka; Miyazaki, Mae; Kaga, Suzuka; Sakaiya, Niina; Kudo, Kyouka; Kimura, Shou; Nakano, Manabu; Fujioka, Miyuki; Yamagata, Kazufumi

doi:10.3233/shti220077

Cited by 2 publications

(2 citation statements)

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“…One study compared the data-augmentation techniques of rotation, scaling, and distortion, and the rotation technique was the only method to improve the diagnostic ability for peripheral blood leukocyte recognition in the eld of hematology. 19 Another study optimized data-augmentation and CNN hyperparameters for detecting coronavirus disease 2019 from chest radiographs regarding validation accuracy. 20 The study evaluated common augmentation techniques in the chest radiograph classi cation literature (resize value, resize method, rotate, zoom, warp, light, ip, and normalize), recently proposed methods (mixup and random erasing), and combinations of these methods.…”

Section: Discussionmentioning

confidence: 99%

Effectiveness of data-augmentation on deep learning in evaluating rapid on-site cytopathology at endoscopic ultrasound-guided fine needle aspiration

Fujii,

Uchida,

Sato

et al. 2023

Preprint

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Rapid on-site cytopathology evaluation (ROSE) has been considered an effective method to increase the diagnostic ability of endoscopic ultrasound-guided fine needle aspiration (EUS-FNA); however, ROSE is unavailable in most institutes worldwide due to the shortage of cytopathologists. To overcome this situation, we created an artificial intelligence (AI)-based system (the ROSE-AI system), which was trained with the augmented data to evaluate the slide images acquired by EUS-FNA. This study aimed to clarify the effects of such data-augmentation on establishing an effective ROSE-AI system by comparing the efficacy of various data-augmentation techniques. The ROSE-AI system was trained with increased data obtained by the various data-augmentation techniques, including geometric transformation, color space transformation, and kernel filtering. By performing five-fold cross-validation, we compared the efficacy of each data-augmentation technique on the increasing diagnostic abilities of the ROSE-AI system. We collected 4,059 divided EUS-FNA slide images from 36 patients with pancreatic cancer and nine patients with non-pancreatic cancer. The diagnostic ability of the ROSE-AI system without data augmentation had a sensitivity, specificity, and accuracy of 87.5%, 79.7%, and 83.7%, respectively. While, some data-augmentation techniques decreased diagnostic ability, the ROSE-AI system trained only with the augmented data using the geometric transformation technique had the highest diagnostic accuracy (88.2%). We successfully developed a prototype ROSE-AI system with high diagnostic ability. Each data-augmentation technique may have various compatibilities with AI-mediated diagnostics, and the geometric transformation was the most effective for the ROSE-AI system.

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Section: Discussionmentioning

confidence: 99%

Effectiveness of data-augmentation on deep learning in evaluating rapid on-site cytopathology at endoscopic ultrasound-guided fine needle aspiration

Fujii,

Uchida,

Sato

et al. 2023

Preprint

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“…This article is based on a study presented by the authors at the 18th World Congress on Medical and Health Informatics [21].…”

Section: Acknowledgmentsmentioning

confidence: 99%

The Effectiveness of Data Augmentation for Mature White Blood Cell Image Classification in Deep Learning — Selection of an Optimal Technique for Hematological Morphology Recognition —

Nozaka

Kamata

Yamagata

2023

IEICE Trans. Inf. & Syst.

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The data augmentation method is known as a helpful technique to generate a dataset with a large number of images from one with a small number of images for supervised training in deep learning. However, a low validity augmentation method for image recognition was reported in a recent study on artificial intelligence (AI). This study aimed to clarify the optimal data augmentation method in deep learning model generation for the recognition of white blood cells (WBCs). Study Design: We conducted three different data augmentation methods (rotation, scaling, and distortion) on original WBC images, with each AI model for WBC recognition generated by supervised training. The subjects of the clinical assessment were 51 healthy persons. Thin-layer blood smears were prepared from peripheral blood and subjected to May-Grünwald-Giemsa staining. Results:The only significantly effective technique among the AI models for WBC recognition was data augmentation with rotation. By contrast, the effectiveness of both image distortion and image scaling was poor, and improved accuracy was limited to a specific WBC subcategory. Conclusion: Although data augmentation methods are often used for achieving high accuracy in AI generation with supervised training, we consider that it is necessary to select the optimal data augmentation method for medical AI generation based on the characteristics of medical images.

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The Effect of Data Augmentation in Deep Learning Approach for Peripheral Blood Leukocyte Recognition

Cited by 2 publications

References 0 publications

Effectiveness of data-augmentation on deep learning in evaluating rapid on-site cytopathology at endoscopic ultrasound-guided fine needle aspiration

Effectiveness of data-augmentation on deep learning in evaluating rapid on-site cytopathology at endoscopic ultrasound-guided fine needle aspiration

The Effectiveness of Data Augmentation for Mature White Blood Cell Image Classification in Deep Learning — Selection of an Optimal Technique for Hematological Morphology Recognition —

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