DiSSECT: Structuring Transfer-Ready Medical Image Representations through Discrete Self-Supervision

• URL: http://arxiv.org/abs/2509.18765v1
• Date: Tue, 23 Sep 2025 07:58:21 GMT
• Title: DiSSECT: Structuring Transfer-Ready Medical Image Representations through Discrete Self-Supervision
• Authors: Azad Singh, Deepak Mishra,
• Abstract summary: DiSSECT is a framework that integrates multi-scale vector quantization into the SSL pipeline to impose a discrete representational bottleneck.<n>It achieves strong performance on both classification and segmentation tasks, requiring minimal or no fine-tuning.<n>We validate DiSSECT across multiple public medical imaging datasets, demonstrating its robustness and generalizability.
• Score: 9.254163621425727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised learning (SSL) has emerged as a powerful paradigm for medical image representation learning, particularly in settings with limited labeled data. However, existing SSL methods often rely on complex architectures, anatomy-specific priors, or heavily tuned augmentations, which limit their scalability and generalizability. More critically, these models are prone to shortcut learning, especially in modalities like chest X-rays, where anatomical similarity is high and pathology is subtle. In this work, we introduce DiSSECT -- Discrete Self-Supervision for Efficient Clinical Transferable Representations, a framework that integrates multi-scale vector quantization into the SSL pipeline to impose a discrete representational bottleneck. This constrains the model to learn repeatable, structure-aware features while suppressing view-specific or low-utility patterns, improving representation transfer across tasks and domains. DiSSECT achieves strong performance on both classification and segmentation tasks, requiring minimal or no fine-tuning, and shows particularly high label efficiency in low-label regimes. We validate DiSSECT across multiple public medical imaging datasets, demonstrating its robustness and generalizability compared to existing state-of-the-art approaches.

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