Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning (2311.17597v2)

Abstract: Self-supervised learning is an efficient pre-training method for medical image analysis. However, current research is mostly confined to specific-modality data pre-training, consuming considerable time and resources without achieving universality across different modalities. A straightforward solution is combining all modality data for joint self-supervised pre-training, which poses practical challenges. Firstly, our experiments reveal conflicts in representation learning as the number of modalities increases. Secondly, multi-modal data collected in advance cannot cover all real-world scenarios. In this paper, we reconsider versatile self-supervised learning from the perspective of continual learning and propose MedCoSS, a continuous self-supervised learning approach for multi-modal medical data. Unlike joint self-supervised learning, MedCoSS assigns different modality data to different training stages, forming a multi-stage pre-training process. To balance modal conflicts and prevent catastrophic forgetting, we propose a rehearsal-based continual learning method. We introduce the k-means sampling strategy to retain data from previous modalities and rehearse it when learning new modalities. Instead of executing the pretext task on buffer data, a feature distillation strategy and an intra-modal mixup strategy are applied to these data for knowledge retention. We conduct continuous self-supervised pre-training on a large-scale multi-modal unlabeled dataset, including clinical reports, X-rays, CT scans, MRI scans, and pathological images. Experimental results demonstrate MedCoSS's exceptional generalization ability across nine downstream datasets and its significant scalability in integrating new modality data. Code and pre-trained weight are available at https://github.com/yeerwen/MedCoSS.

• The paper introduced MedCoSS, a novel continual self-supervised learning framework that overcomes modal data collision with a stage-wise pre-training approach.
• It employs rehearsal-based techniques and intra-modal mixup to prevent catastrophic forgetting while enhancing representation across varied modalities.
• Experimental results across nine clinical tasks show significant improvements in metrics like AUC, accuracy, F1 score, Dice coefficient, and Hausdorff distance.

Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning

In recent developments, self-supervised learning (SSL) has emerged as a prominent pre-training method for medical image analysis due to its potential for high-quality representation learning without the necessity for labeled data. However, existing methods in this domain are largely constrained to specific modalities, thus lacking the universality required to seamlessly transfer learned representations across various modalities. This paper introduces a novel approach termed "Continual Self-supervised Learning" using the MedCoSS framework, effectively addressing the limitations of existing methods by facilitating universal multi-modal medical data representation learning.

Methodology Overview

The MedCoSS paradigm innovatively combines principles of self-supervised learning with techniques from continual learning. It addresses the challenges inherent in simply combining datasets from different modalities into a joint learning process. The paper identifies the prominent issue of modal data collision, where the inclusion of multiple modalities in a single training stage may lead to conflicting representations and degraded performance across tasks. MedCoSS circumvents these challenges through a sequential, stage-wise learning approach, allocating dedicated pre-training stages to different modalities. This is augmented by rehearsal-based continual learning techniques that incorporate a $k$-means sampling strategy to select representative data samples for rehearsal, preventing catastrophic forgetting of previous knowledge. Feature distillation and intra-modal mixup strategies are further employed to bolster knowledge retention during subsequent stages of training.

Experimental Evaluation

The experimental framework employed by the authors involves the use of a large-scale, multi-modal dataset encompassing medical data from various modalities such as clinical reports, X-rays, CT, MRI, and pathological images. The paper reports significant improvements in generalization performance across nine downstream tasks, covering multiple datasets and a range of clinical applications. Notably, the paper demonstrates this performance using various metrics such as AUC, accuracy, F1 score, Dice similarity coefficient, and Hausdorff distance, ensuring a comprehensive evaluation of their proposed method.

Implications and Future Directions

The implications of this research are manifold. Practically, MedCoSS offers a scalable solution for universal medical pre-training, efficiently integrating data from diverse modalities while maintaining robust performance across tasks. Theoretically, the incorporation of continual learning strategies within SSL frameworks provides a promising avenue for overcoming the limitations of traditional joint training methods, particularly in scenarios characterized by dynamic, stream-like data acquisition. Such an approach could be envisioned as a step towards the development of AI systems capable of performing at a comparable level across a wide variety of medical imaging modalities.

In future research, extending the MedCoSS framework to adapt dynamically to varying data stream characteristics remains a captivating challenge, with potential applications in real-time clinical settings where new data modalities become available progressively. Moreover, exploring the integration of additional modalities and extending the continual learning framework in more complex environments will be vital for further advancing the efficacy and applicability of multi-modal models in medical domains.

In conclusion, the paper presents a methodologically sound approach to achieving universal multi-modal data representation learning, crucially advancing the field of medical deep learning. Its contributions lie in the intelligent integration of self-supervised learning paradigms with continual learning techniques, setting the stage for future innovations in multi-modal medical data analysis.