Learning More with Less

Han, Changhee; Murao, Kohei; Noguchi, Tomoyuki; Kawata, Yusuke; Uchiyama, Fumiya; Rundo, Leonardo; Nakayama, Hideki; Satoh, Shin’ichi

doi:10.1145/3357384.3357890

Cited by 71 publications

(11 citation statements)

References 31 publications

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“…To alleviate the problems related to the basic data augmentation approaches (including the problem of generating correlated data samples), various approaches toward generating artificial data (GAD) have been proposed. Generative adversarial networks (GANs), originally introduced in Goodfellow et al (2014), are being exploited to augment medical datasets (Han et al, 2019;Shorten and Khoshgoftaar, 2019). The main objective of a GAN ( Figure 6) is to generate a new data example (by a generator) which will be indistinguishable from the real data by the 2 These variations can be however alleviated by appropriate data standardization.…”

Section: Data Augmentation By Generating Artificial Datamentioning

confidence: 99%

Data Augmentation for Brain-Tumor Segmentation: A Review

Nalepa

Marcinkiewicz²,

Kawulok

2019

Front. Comput. Neurosci.

245

127

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Data augmentation is a popular technique which helps improve generalization capabilities of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal role in scenarios in which the amount of high-quality ground-truth data is limited, and acquiring new examples is costly and time-consuming. This is a very common problem in medical image analysis, especially tumor delineation. In this paper, we review the current advances in data-augmentation techniques applied to magnetic resonance images of brain tumors. To better understand the practical aspects of such algorithms, we investigate the papers submitted to the Multimodal Brain Tumor Segmentation Challenge (BraTS 2018 edition), as the BraTS dataset became a standard benchmark for validating existent and emerging brain-tumor detection and segmentation techniques. We verify which data augmentation approaches were exploited and what was their impact on the abilities of underlying supervised learners. Finally, we highlight the most promising research directions to follow in order to synthesize high-quality artificial brain-tumor examples which can boost the generalization abilities of deep models.

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Section: Data Augmentation By Generating Artificial Datamentioning

confidence: 99%

Data Augmentation for Brain-Tumor Segmentation: A Review

Nalepa

Marcinkiewicz²,

Kawulok

2019

Front. Comput. Neurosci.

245

127

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“…To solve this issue, image augmentation and transfer learning techniques can be used [57]. In this light, GANs can be used to generate synthetic additional training instances [58].…”

Section: Deep Learning In the Medical Image Application Domainmentioning

confidence: 99%

AI applications to medical images: From machine learning to deep learning

Rundo²,

et al. 2021

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Artificial intelligence (AI) models are playing an increasing role in biomedical research and healthcare services. This review focuses on challenges points to be clarified about how to develop AI applications as clinical decision support systems in the real-world context. Methods: A narrative review has been performed including a critical assessment of articles published between 1989 and 2021 that guided challenging sections. Results: We first illustrate the architectural characteristics of machine learning (ML)/radiomics and deep learning (DL) approaches. For ML/radiomics, the phases of feature selection and of training, validation, and testing are described. DL models are presented as multi-layered artificial/convolutional neural networks, allowing us to directly process images. The data curation section includes technical steps such as image labelling, image annotation (with segmentation as a crucial step in radiomics), data harmonization (enabling compensation for differences in imaging protocols that typically generate noise in non-AI imaging studies) and federated learning. Thereafter, we dedicate specific sections to: sample size calculation, considering multiple testing in AI approaches; procedures for data augmentation to work with limited and unbalanced datasets; and the interpretability of AI models (the so-called black box issue). Pros and cons for choosing ML versus DL to implement AI applications to medical imaging are finally presented in a synoptic way. Conclusions: Biomedicine and healthcare systems are one of the most important fields for AI applications and medical imaging is probably the most suitable and promising domain. Clarification of specific challenging points facilitates the development of such systems and their translation to clinical practice.

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“…On the other hand, artificial data generation [67,68] exploits the Generative adversarial networks (GANs) [69] to generate realistic data that are indistinguishable from the real data and also serves as a effective method for data anonymization [66]. GANs are able to generate a wide variety of realistic samples that can bring invariance and robustness.…”

Section: Data Augmentationmentioning

confidence: 99%

Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

2021

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Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.

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Learning More with Less

Cited by 71 publications

References 31 publications

Data Augmentation for Brain-Tumor Segmentation: A Review

Data Augmentation for Brain-Tumor Segmentation: A Review

AI applications to medical images: From machine learning to deep learning

Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

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