Ziduo Yang

Zhaoshan Liu, Qiujie Lv, Yifan Li et al.

Data augmentation (DA) has been widely leveraged in computer vision to alleviate the data shortage, whereas the DA in medical image analysis (MIA) faces multiple challenges. The prevalent DA approaches in MIA encompass conventional DA, synthetic DA, and automatic DA. However, utilizing these approaches poses various challenges such as experience-driven design and intensive computation cost. Here, we propose an efficient and effective automatic DA method termed MedAugment. We propose a pixel augmentation space and spatial augmentation space and exclude the operations that can break medical details and features, such as severe color distortions or structural alterations that can compromise image diagnostic value. Besides, we propose a novel sampling strategy by sampling a limited number of operations from the two spaces. Moreover, we present a hyperparameter mapping relationship to produce a rational augmentation level and make the MedAugment fully controllable using a single hyperparameter. These configurations settle the differences between natural and medical images, such as high sensitivity to certain attributes such as brightness and posterize. Extensive experimental results on four classification and four segmentation datasets demonstrate the superiority of MedAugment. Compared with existing approaches, the proposed MedAugment serves as a more suitable yet general processing pipeline for medical images without producing color distortions or structural alterations and involving negligible computational overhead. We emphasize that our method can serve as a plugin for arbitrary projects without any extra training stage, thereby holding the potential to make a valuable contribution to the medical field, particularly for medical experts without a solid foundation in deep learning. Code is available at https://github.com/NUS-Tim/MedAugment.

Zhaoshan Liu, Qiujie Lv, Ziduo Yang et al.

The transformer is primarily used in the field of natural language processing. Recently, it has been adopted and shows promise in the computer vision (CV) field. Medical image analysis (MIA), as a critical branch of CV, also greatly benefits from this state-of-the-art technique. In this review, we first recap the core component of the transformer, the attention mechanism, and the detailed structures of the transformer. After that, we depict the recent progress of the transformer in the field of MIA. We organize the applications in a sequence of different tasks, including classification, segmentation, captioning, registration, detection, enhancement, localization, and synthesis. The mainstream classification and segmentation tasks are further divided into eleven medical image modalities. A large number of experiments studied in this review illustrate that the transformer-based method outperforms existing methods through comparisons with multiple evaluation metrics. Finally, we discuss the open challenges and future opportunities in this field. This task-modality review with the latest contents, detailed information, and comprehensive comparison may greatly benefit the broad MIA community.