LViT: Language meets Vision Transformer in Medical Image Segmentation (2206.14718v4)

Abstract: Deep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the challenge of obtaining sufficient high-quality labeled data due to the prohibitive data annotation cost. To alleviate this limitation, we propose a new text-augmented medical image segmentation model LViT (Language meets Vision Transformer). In our LViT model, medical text annotation is incorporated to compensate for the quality deficiency in image data. In addition, the text information can guide to generate pseudo labels of improved quality in the semi-supervised learning. We also propose an Exponential Pseudo label Iteration mechanism (EPI) to help the Pixel-Level Attention Module (PLAM) preserve local image features in semi-supervised LViT setting. In our model, LV (Language-Vision) loss is designed to supervise the training of unlabeled images using text information directly. For evaluation, we construct three multimodal medical segmentation datasets (image + text) containing X-rays and CT images. Experimental results show that our proposed LViT has superior segmentation performance in both fully-supervised and semi-supervised setting. The code and datasets are available at https://github.com/HUANGLIZI/LViT.

• The paper introduces LViT with a hybrid CNN-Transformer design that integrates text and image data for enhanced medical segmentation.
• It employs innovative techniques like Exponential Pseudo label Iteration and a specialized LV Loss to refine segmentation in semi-supervised settings.
• Experimental results show LViT outperforms current models, achieving metrics such as 74.57% Dice and 61.33% mIoU on key datasets.

Overview of LViT: Integrating Language and Vision for Medical Image Segmentation

The paper entitled "LViT: Language meets Vision Transformer in Medical Image Segmentation" introduces a novel approach aimed at enhancing the performance of medical image segmentation through the integration of textual data with visual data. The main contribution of this work is the introduction of LViT, a language-augmented vision transformer model designed specifically for medical image segmentation tasks. This model capitalizes on the synergy between medical images and their associated text data, offering a significant improvement in segmentation performance, particularly in scenarios with limited labeled data.

Challenges in Medical Image Segmentation

In the field of medical image segmentation, obtaining sufficient high-quality labeled data represents a significant challenge due to the high cost and time-consuming nature of data annotation. The complexity of medical images, compounded by varied tissue structures and indistinct boundaries, often complicates accurate segmentation. While deep learning models have shown promise in automating these tasks, their reliance on substantial labeled datasets limits their applicability in real-world clinical settings.

Contributions of the LViT Model

The key innovation of LViT lies in its unique architecture and methodology that marries the strengths of both textual and visual data:

1. Architecture: LViT employs a double-U structure that integrates a U-shaped CNN with a U-shaped Transformer network. This design facilitates the concurrent processing of image and text information. By leveraging a hybrid CNN-Transformer structure with Pixel-Level Attention Modules (PLAM), LViT retains the CNN's prowess in extracting local image features while utilizing the Transformer to encode global context and text information.
2. Text Annotation Integration: Unlike traditional segmentation approaches, LViT introduces medical text annotation into the segmentation framework. Textual data, which often accompanies medical images in clinical records, is leveraged to generate pseudo labels, thereby augmenting the quality of training data in a semi-supervised learning context. This approach enables the model to benefit from domain-specific expert knowledge inherent in the text annotations.
3. Exponential Pseudo label Iteration (EPI) Mechanism: To address the challenge of improving pseudo label quality in semi-supervised settings, the authors propose the EPI mechanism. This innovative approach utilizes an Exponential Moving Average (EMA) process to iteratively refine pseudo labels, enhancing their reliability and thus improving the model's performance.
4. LV Loss: LViT introduces the Language-Vision loss, a tailored loss function that directly supervises the training of unlabeled images using textual information. This enhances consistency and convergence, particularly when dealing with partial annotations.

Experimental Results and Implications

The evaluation of LViT was performed using three multimodal medical segmentation datasets encompassing X-rays and CT images. The experimental results demonstrate that LViT surpasses existing state-of-the-art models across both fully-supervised and semi-supervised benchmarks. Notably, LViT achieved 74.57% Dice score and 61.33% mIoU on the MosMedData+ dataset. Even with reduced training label ratios, LViT demonstrates competitive performance, underscoring the efficacy of text augmentation in data-scarce environments.

These findings suggest significant practical implications for enhancing medical image segmentation without the prohibitive costs associated with exhaustive manual annotations. The integration of linguistic and visual information could potentially generalize to other domains where multimodal data is available, paving the way for future developments in AI applications that leverage complementary data sources.

Future Directions

The LViT model presents foundational advancements that invite further exploration. Future efforts may focus on extending the model to 3D segmentation tasks, particularly for volumetric medical images such as MRIs, where spatial and text information could further enhance outcomes. Additionally, automating the generation of structured text from images during the inference stage could broaden the model's applicability, allowing it to function independently of text inputs where none are available.

Overall, this paper contributes an innovative approach to overcoming data annotation constraints in medical image segmentation, establishing a promising direction for advancements in AI models that successfully integrate multimodal information.