To what extent does NOVA's anomaly localization accuracy degrade when tested on out-of-distribution brain MRI

Abstract

Abstract: In many real-world applications, deployed models encounter inputs that differ from the data seen during training. Out-of-distribution detection identifies whether an input stems from an unseen distribution, while open-world recognition flags such inputs to ensure the system remains robust as ever-emerging, previously \$unknown\$ categories appear and must be addressed without retraining. Foundation and vision-language models are pre-trained on large and diverse datasets with the expectation of broad generalization across domains, including medical imaging. However, benchmarking these models on t

Research Question

To what extent does NOVA's anomaly localization accuracy degrade when tested on out-of-distribution brain MRI data from different scanner manufacturers and imaging protocols compared to in-distribution performance?

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Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance