Towards Modality-Agnostic Medical Image Anomaly Detection: A Training-Free Manifold Refinement Approach
arXiv:2604.19191v2 Announce Type: replace-cross Abstract: Deploying AI-based anomaly detection across diverse clinical imaging settings remains challenging because most existing methods rely on modality-specific architectures, anatomical priors, or extensive retraining, limiting their use as general-purpose screening tools. One-class classification (OCC) offers a label-efficient alternative by training exclusively on normal data, but conventional two-stage pipelines fit a density estimator directly on raw pretrained embeddings, leaving substantial discriminative structure in the latent space u
The proliferation of AI in medical imaging necessitates more generalized and robust solutions for anomaly detection as deployment scales across diverse clinical settings.
A modality-agnostic approach to medical image anomaly detection reduces reliance on extensive labeling and retraining, making AI-based diagnostic tools more efficient and widely deployable.
The ability to deploy anomaly detection AI across various medical imaging modalities without significant, specialized retraining of 'black-box' systems simplifies adoption and expands accessibility.
- · Medical AI developers
- · Healthcare providers
- · Patients in diverse clinical settings
- · Medical diagnostic imaging
- · Specialized, modality-specific AI development firms
- · Manual image analysis workflows
More efficient and scalable deployment of AI for detecting abnormalities in medical images without extensive manual labeling.
Reduced costs and increased accessibility of advanced diagnostic capabilities, particularly in regions with limited specialist resources.
Acceleration of AI integration into broader clinical practice, potentially leading to earlier disease detection and improved treatment outcomes.
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Read at arXiv cs.AI