Author

M. M. Ghazi

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Preprint Aug 2026

How Many Labels Are Enough? ALDA: Active Learning Deployment Advisor for Medical Image Classification

Active learning (AL) promises to reduce the cost of medical imaging projects by lowering the number of clinical labels required. However, practical deployment requires committing to a sampling strategy before the full annotation budget is spent, and choosing the wrong strategy can increase rather than decrease costs. W...

Julia Machnio, Mads Nielsen, M. M. Ghazi · 0 citations

#artificial intelligence Preprint Sep 2026

QINA: Quantum-Inspired Nonlinear Adapters for Pretrained Vision Models

Adapting large pretrained vision models under limited data and frozen-backbone constraints remains a central challenge in transfer learning. While lightweight adapters and parameter-efficient fine-tuning methods are widely adopted, most rely on generic multilayer perceptrons or low-rank linear updates, offering limited...

M. M. Ghazi · 0 citations

#artificial intelligence Preprint Aug 2026

ActiveAugment: Online Active Learning for Augmentation Selection in Deep Learning

This work introduces ActiveAugment, a unified framework that treats augmentation selection as an online active learning problem, and reveals that the augmentation selection policy evolves meaningfully during training and that strategy choice has a direct impact on generalisation.

Noah Videcrantz, M. M. Ghazi · 0 citations