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arXiv Machine Learning By Cecilia Diana-Albelda, Fiona Kekwick, Raghav Mehta, Aleksandra Kulbaka, Paul M. Matthews, Jesus Bescos, \'Alvaro Garc\'ia-Mart\'in, Wenjia Bai, Bernhard Kainz

Convergent Performance, Divergent Mechanisms in LoRA-Adapted Vision Foundation Models for Brain MRI

Read the original on arXiv Machine Learning →

The study examines how LoRA adaptation of four Vision Foundation Models—DINOv3, SigLIP2, MerMED-FM, and BiomedCLIP—affects brain MRI tasks. Despite achieving similar segmentation accuracy (mean Dice 78‑81%), the models exhibit distinct internal dynamics, with early‑to‑mid layers being most crucial for segmentation. Restricting LoRA to these layers yields comparable performance while mitigating negative impacts on Alzheimer’s disease classification.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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