arXiv:2607. 09102v1 Announce Type: cross Abstract: Medical imaging models are often deployed without the demographic, acquisition, and quality metadata needed for subgroup auditing.
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arXiv:2607. 02596v1 Announce Type: cross Abstract: Deep learning models for medical diagnosis frequently exhibit substantial performance disparities across sensitive subgroups (e.
arXiv:2608. 00073v1 Announce Type: cross Abstract: Rigorous dataset partitioning is a foundational, yet frequently overlooked, prerequisite for reliable deep learning in longitudinal medical imaging.
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.
arXiv:2411. 05824v4 Announce Type: replace-cross Abstract: Medical Image Analysis (MedIA) has become indispensable in modern healthcare, enhancing clinical diagnostics and personalized treatment.
arXiv:2608. 12035v1 Announce Type: cross Abstract: Deploying unsupervised domain adaptation (UDA) in clinical practice requires choosing which algorithm to use and which of its trained models to ship.
arXiv:2607. 20641v1 Announce Type: new Abstract: Federated learning (FL) enables multiple clinical institutions to collaboratively train a shared disease classifier without centralizing patient data.