TransMed: Transformers Advance Multi-modal Medical Image Classification (2103.05940v1)

Abstract: Over the past decade, convolutional neural networks (CNN) have shown very competitive performance in medical image analysis tasks, such as disease classification, tumor segmentation, and lesion detection. CNN has great advantages in extracting local features of images. However, due to the locality of convolution operation, it can not deal with long-range relationships well. Recently, transformers have been applied to computer vision and achieved remarkable success in large-scale datasets. Compared with natural images, multi-modal medical images have explicit and important long-range dependencies, and effective multi-modal fusion strategies can greatly improve the performance of deep models. This prompts us to study transformer-based structures and apply them to multi-modal medical images. Existing transformer-based network architectures require large-scale datasets to achieve better performance. However, medical imaging datasets are relatively small, which makes it difficult to apply pure transformers to medical image analysis. Therefore, we propose TransMed for multi-modal medical image classification. TransMed combines the advantages of CNN and transformer to efficiently extract low-level features of images and establish long-range dependencies between modalities. We evaluated our model for the challenging problem of preoperative diagnosis of parotid gland tumors, and the experimental results show the advantages of our proposed method. We argue that the combination of CNN and transformer has tremendous potential in a large number of medical image analysis tasks. To our best knowledge, this is the first work to apply transformers to medical image classification.

TransMed: Transformers Advance Multi-modal Medical Image Classification

The paper "TransMed: Transformers Advance Multi-modal Medical Image Classification" by Yin Dai and Yifan Gao investigates the application of transformers to the challenging domain of multi-modal medical image classification. This research explores the integration of convolutional neural networks (CNNs) and transformers to leverage both local feature extraction capabilities and long-range relationship modeling in medical images. The paper provides a comprehensive method known as TransMed, which merges the strengths of CNNs and transformers to improve classification tasks related to parotid gland tumors.

Key Contributions

1. Application of Transformers in Medical Image Classification: This paper is notable for being one of the first efforts to harness transformer architectures for the classification of medical images. The paper builds on recent advances in computer vision, where transformers have shown promise, and adapts these architectures to handle the complexities associated with multi-modal medical data.
2. Development of a Multi-modal Image Fusion Strategy: The authors introduce a novel strategy for multi-modal image fusion that captures mutual information across different modalities more effectively. This strategy capitalizes on the ability of transformers to model dependencies across sequences, filling a void left by traditional fusion techniques that often struggle with modality interaction and computational efficiency.
3. Performance Evaluation and Results: TransMed is evaluated on a dataset consisting of MRI scans of parotid gland tumors. The model demonstrates superior performance over existing methods, achieving an accuracy of up to 88.9% in classification tasks. Its efficiency is underscored by lower computational costs compared to other state-of-the-art frameworks. TransMed variants, including TransMed-T (tiny) and TransMed-S (small), are tested, showing a marked improvement in classification precision across tumor classes such as Pleomorphic Adenoma and Warthin Tumor.

Architectural Insights

The architecture of TransMed is a hybrid model comprised of two main components: a CNN branch for low-level feature extraction and a transformer branch for establishing long-range dependencies. The CNN processes multi-modal images to generate patch embeddings, while the transformer encodes these embeddings to model their inter-relationships effectively. This dual-branch approach allows TransMed to surpass the constraints faced by models relying solely on either CNNs or transformers.

Implications and Future Directions

The integration of transformers into medical image analysis, as exemplified by TransMed, carries significant implications. The research suggests that such hybrid models can overcome the limitations of CNN-only architectures, particularly in handling small-scale medical datasets. The promise of transformers in this domain opens avenues for further studies into their use in various medical imaging tasks beyond classification, including segmentation and detection.

Future work could explore the adaptation of TransMed for larger medical datasets, potentially incorporating more complex transformer architectures that have proved effective in NLP and general computer vision tasks. Moreover, expanding this research to cover other types of multimodal medical data, like CT or PET scans in combination with MRI, could further exploit the capabilities of transformers and improve diagnostic tools in clinical settings.

In summary, the TransMed framework represents a significant step forward in the application of transformers to multi-modal medical image analysis. It highlights the potential of such models to enhance diagnostic accuracy and offers a robust foundation for subsequent research and development in transformer-based methodologies for medical applications.