Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review (2301.03505v3)

Abstract: The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in Computer Vision. Among other merits, Transformers are witnessed as capable of learning long-range dependencies and spatial correlations, which is a clear advantage over convolutional neural networks (CNNs), which have been the de facto standard in Computer Vision problems so far. Thus, Transformers have become an integral part of modern medical image analysis. In this review, we provide an encyclopedic review of the applications of Transformers in medical imaging. Specifically, we present a systematic and thorough review of relevant recent Transformer literature for different medical image analysis tasks, including classification, segmentation, detection, registration, synthesis, and clinical report generation. For each of these applications, we investigate the novelty, strengths and weaknesses of the different proposed strategies and develop taxonomies highlighting key properties and contributions. Further, if applicable, we outline current benchmarks on different datasets. Finally, we summarize key challenges and discuss different future research directions. In addition, we have provided cited papers with their corresponding implementations in https://github.com/mindflow-institue/Awesome-Transformer.

Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review

The paper encapsulates the growing synergy between Vision Transformers (ViTs) and medical image analysis, delivering an extensive overview of how these novel architectures have been integrated across diverse applications within the field. Rooted in the Transformer architecture's success in NLP for learning long-range dependencies and spatial correlations, this review pivots to its implications in computer vision, and more specifically, medical imaging.

The authors provide a methodical review of various medical imaging tasks enhanced by Vision Transformers, including classification, segmentation, detection, registration, synthesis, and clinical report generation. For each task, the authors delineate the recent advancements alongside their strengths and weaknesses, categorize existing strategies, and outline the current benchmarks. Furthermore, cited papers and corresponding implementations are accessible via a GitHub repository, fostering further innovation in the community.

Key Insights and Findings

1. Transformer Integration Across Modalities and Tasks

ViTs have been employed broadly across different imaging modalities such as CT, MRI, X-ray, and ultrasound, owing to their ability to model global relationships in the data effectively. Notably, in medical image classification, ViTs demonstrate superiority in handling 3D data and achieving generalization even in limited-data scenarios, often outperforming convolutional models in tasks ranging from retinal disease classification to COVID-19 detection in CXR images.

2. Segmentation and Synthesis

In segmentation, transformers alleviate the obstacles posed by CNNs' limited receptive fields by ensuring coherent segmentation of high-resolution medical images. The deployment of architectures like Swin-Unet and TransDeepLab illustrates success in balancing local and global information. Conversely, in synthesis tasks, transformers excel in generating or enhancing medical images through models like ResViT that merge local-global contextual features efficiently.

3. Detection and Registration

Detection tasks benefit from transformers' capacity for precise localization and context modeling, showcased in systems like CellCentroidFormer. Concurrently, ViT-based approaches in registration, such as TransMorph and Dual Transformer Network, leverage transformers to perform both affine and deformable transformations accurately, achieving efficient spatial alignment across diverse scanning modalities.

4. Clinical Report Generation

In generating clinical reports, transformers integrate multimodal data effectively, illustrating the ability to produce comprehensive, clinically relevant narratives. Approaches like CMN and CGRG employ cross-modal alignment and memory networks to unify visual and textual information, enhancing the granularity and coherence of predictive narratives.

Challenges and Future Directions

Despite these advances, the utilization of transformers presents several challenges, primarily related to computational complexity and data-hungry nature. Training large transformer models requires extensive computational resources, which can be a frontier for practical deployment in real-world medical systems. Addressing these issues through efficient architecture designs, leveraging pre-trained models, and enhancing cross-task generalization remains an open research area.

The paper posits potential future directions, emphasizing improvements in model explainability, which is crucial for clinical adoption, and developing efficient transformer architectures optimized for edge devices. The fusion of self-supervised learning approaches with transformers is also highlighted as a prospective avenue to mitigate the reliance on extensive labeled datasets.

Conclusion

This comprehensive review underscores the potential of Vision Transformers to transform medical image analysis, offering compelling improvements in performance across various image data and tasks. By systematically analyzing existing literature and outcomes, the paper offers a foundational insight and inspires further exploration and refinement of these architectures within the medical field, alluding to transformative implications in enhancing diagnostic accuracy and efficiency.