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...
C. U. Harsy, Tassilo Wald, Karol Gotkowski et al.· 0 citations
Magnetic Resonance Imaging (MRI) interpretation is fundamental to clinical decision-making, requiring radiologists to integrate multi-view anatomical planes across sequential timepoints while precisely localizing interval changes. However, existing vision-language benchmarks remain confined to single-timepoint, single-...
Wafa Al Ghallabi, Ritesh Thawkar, Sara Ghaboura et al.· 0 citations
Results show that forgetting depends not only on how much the model changes, but also on which parts of the model are allowed to change, which means that forgetting still increases as more blocks are trained and remains severe when the full backbone is updated.
Amal Saqib, Tausifa Jan Saleem, N. Saeed et al.· 0 citations
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