TL;DR
Findings across the reviewed studies suggest that pretraining with RadImageNet produces feature representations that are more aligned with radiological image characteristics than those derived from general-purpose datasets.
Satellite imagery is proposed as a novel pretraining domain for MedVFM development and benchmarking, motivated by its closer visual alignment with medical data and its freedom from the privacy constraints that limit medical datasets.
Radiological artificial intelligence has advanced rapidly, yet most systems remain narrowly task-specific, data-intensive, and fragile under domain shift. Foundation models promise more transferable and data-efficient solutions, but existing approaches are limited in scale, evaluated narrowly, and often assume that a s...
Reliable categorization of brain tumors from magnetic resonance imaging (MRI) is a prerequisite for timely clinical intervention, yet the great majority of transfer-learning studies in this space report a single backbone in isolation, leaving open the question of how much of the reported performance is attributable to...
Major deep learning architectures, including CNNs, residual networks, UNet, attention-based models, Vision Transformers, and hybrid approaches, along with their clinical applications are summarized and emerging directions such as self-supervised learning, Explainable AI, federated learning, and lightweight models are h...
For small-sample medical image classification, this work recommends cross-validation-based HPO when computational resources permit because it trades additional computation for a more reliable development-time estimate of subsequent test performance.
A Cross-Domain Generalization Neural Architecture Search (CDG-NAS) framework that automatically discovers neural network architectures capable of maintaining high diagnostic accuracy under diverse clinical domain shifts and incorporates domain robustness as a first-class optimization objective throughout the search pro...
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