GSDA: Generative adversarial network-based semi-supervised data augmentation for ultrasound image classification

Liu, Zhaoshan; Lv, Qiujie; Lee, Chau Hung; Shen, Lei

doi:10.1016/j.heliyon.2023.e19585

Cited by 4 publications

(1 citation statement)

References 31 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hybrid approaches involve integrating diverse methodologies, including consistency regularization [50][51][52][53], pseudo-labeling [39,54], data augmentation [55][56][57][58][59], entropy estimation [60,61], and other elements [62][63][64], to enhance performance. In the upcoming Section 4.6, we will examine different categories of hybrid methods.…”

Section: Hybrid Methodsmentioning

confidence: 99%

A Critical Analysis of Deep Semi-Supervised Learning Approaches for Enhanced Medical Image Classification

Shakya,

Alavi,

Porteous

et al. 2024

Information

View full text Add to dashboard Cite

Deep semi-supervised learning (DSSL) is a machine learning paradigm that blends supervised and unsupervised learning techniques to improve the performance of various models in computer vision tasks. Medical image classification plays a crucial role in disease diagnosis, treatment planning, and patient care. However, obtaining labeled medical image data is often expensive and time-consuming for medical practitioners, leading to limited labeled datasets. DSSL techniques aim to address this challenge, particularly in various medical image tasks, to improve model generalization and performance. DSSL models leverage both the labeled information, which provides explicit supervision, and the unlabeled data, which can provide additional information about the underlying data distribution. That offers a practical solution to resource-intensive demands of data annotation, and enhances the model’s ability to generalize across diverse and previously unseen data landscapes. The present study provides a critical review of various DSSL approaches and their effectiveness and challenges in enhancing medical image classification tasks. The study categorized DSSL techniques into six classes: consistency regularization method, deep adversarial method, pseudo-learning method, graph-based method, multi-label method, and hybrid method. Further, a comparative analysis of performance for six considered methods is conducted using existing studies. The referenced studies have employed metrics such as accuracy, sensitivity, specificity, AUC-ROC, and F1 score to evaluate the performance of DSSL methods on different medical image datasets. Additionally, challenges of the datasets, such as heterogeneity, limited labeled data, and model interpretability, were discussed and highlighted in the context of DSSL for medical image classification. The current review provides future directions and considerations to researchers to further address the challenges and take full advantage of these methods in clinical practices.

show abstract