vox2vec: A Framework for Self-supervised Contrastive Learning of Voxel-level Representations in Medical Images

Abstract: This paper introduces vox2vec - a contrastive method for self-supervised learning (SSL) of voxel-level representations. vox2vec representations are modeled by a Feature Pyramid Network (FPN): a voxel representation is a concatenation of the corresponding feature vectors from different pyramid levels. The FPN is pre-trained to produce similar representations for the same voxel in different augmented contexts and distinctive representations for different voxels. This results in unified multi-scale representations that capture both global semantics (e.g., body part) and local semantics (e.g., different small organs or healthy versus tumor tissue). We use vox2vec to pre-train a FPN on more than 6500 publicly available computed tomography images. We evaluate the pre-trained representations by attaching simple heads on top of them and training the resulting models for 22 segmentation tasks. We show that vox2vec outperforms existing medical imaging SSL techniques in three evaluation setups: linear and non-linear probing and end-to-end fine-tuning. Moreover, a non-linear head trained on top of the frozen vox2vec representations achieves competitive performance with the FPN trained from scratch while having 50 times fewer trainable parameters. The code is available at https://github.com/mishgon/vox2vec .

• The paper introduces vox2vec, a novel self-supervised framework that leverages contrastive learning with an FPN architecture for detailed, multi-scale CT image representations.
• It achieves state-of-the-art segmentation results on 22 tasks using only about 2% of trainable parameters compared to traditional end-to-end models.
• This approach reduces reliance on extensive annotations and paves the way for scalable, adaptable medical image analysis.

Summary of "vox2vec: A Framework for Self-supervised Contrastive Learning of Voxel-level Representations in Medical Images"

"vox2vec: A Framework for Self-supervised Contrastive Learning of Voxel-level Representations in Medical Images" introduces a methodology for self-supervised voxel-level representation learning in medical imaging using a contrastive approach. This paper proposes an innovative framework, vox2vec, that leverages a Feature Pyramid Network (FPN) to capture both local and global semantic information from voxel-level data. The approach stands out in its ability to generate contextually aware multi-scale representations that are effective across various segmentation tasks and enhances the generalization capabilities of models trained for medical image analysis.

Research Contributions

1. Framework Introduction: vox2vec is presented as a contrastive self-supervised learning framework tailored for voxel-level representation of medical images using computed tomography (CT). Leveraging contrastive learning, vox2vec produces similar representations for voxels in varying contexts, enhancing contextual understanding and segmentation quality.
2. Use of Feature Pyramid Network (FPN): A distinctive aspect of vox2vec is the utilization of the FPN architecture. This design choice is critical as it creates high-dimensional, voxel-wise representations through concatenation of the corresponding feature vectors across multiple pyramid levels, enabling higher resolution and scale-agnostic understanding required in medical imaging.
3. Evaluation Protocol and Results: The framework's efficacy is demonstrated through pre-training on over 6,500 publicly available CT images and evaluating the model on 22 segmentation tasks. Results indicate that vox2vec substantially outperforms existing self-supervised learning methods in medical imaging across varied evaluation setups, notably in linear and non-linear probing scenarios.

Numerical Performance and Comparison

vox2vec achieved superior performance metrics compared to the state-of-the-art (SotA) models, particularly when evaluated under linear and non-linear probing conditions. For instance, vox2vec's performance in linear probing was markedly higher with significant parameter efficiency, achieving competitive results with only about 2% of the trainable parameters compared to traditional methods in end-to-end setups. This efficiency affirms the robustness and scalability of the self-supervised pre-training model, particularly useful in domains requiring minimal labeled data.

Implications and Future Directions

The implications of such a framework are significant for the medical imaging domain. The reduced dependency on annotated data and the capability to generalize across multiple domains without extensive re-training mark a substantial leap in resource optimization and model deployment in real-world clinical settings. Furthermore, the observation that the frozen representations achieve close performance to fully fine-tuned models underlines the potential of vox2vec to streamline model updates and maintenance in dynamic environments.

Future research could focus on expanding the pre-training corpus to leverage more diverse medical data and assessing the framework's applicability to other imaging modalities beyond CT. Additionally, exploring domain adaptation and few-shot learning scenarios using vox2vec may unravel further enhancement in model robustness and adaptability. The scalability of the vox2vec framework with increasing dataset size and model complexity also remains an exciting avenue for further exploration.

Overall, this paper provides a detailed and methodologically sound exploration into voxel-level self-supervised learning, presenting practical advancements for real-world medical image processing applications. The merger of contrastive learning techniques with the efficient use of FPN architecture sets an educational precedent and offers a template for further research in computational medical imaging.