Joint self-supervised image-volume representation learning with intra-inter contrastive clustering

Sun, 01 Jan 2023 00:00:00 +0000

Lvm-med: Learning large-scale self-supervised vision models for medical imaging via second-order graph matching

Sun, 01 Jan 2023 00:00:00 +0000

Large vision models for robust and trustworthy medical image understanding

Sat, 01 Jan 2022 00:00:00 +0000

This collaboration investigates the applicability of large vision models to medical imaging, addressing robustness, generalization, and trustworthiness under domain shift and limited supervision. The work examined the behavior of foundation-scale models transferred from natural-image pretraining to medical imaging tasks. Methodological contributions include empirical analyses of cross-domain generalization, sensitivity to spurious correlations, and limitations of naïve scaling, alongside proposed evaluation protocols and architectural adaptations to improve robustness and reliability in medical contexts.

Perspective

The collaboration supported doctoral mentorship and strengthened national expertise at the intersection of foundation models and medical imaging. The insights gained inform ongoing work on principled evaluation and responsible deployment of large vision models in medical applications.

Duy M. H. Nguyen |

Joint self-supervised image-volume representation learning with intra-inter contrastive clustering

Lvm-med: Learning large-scale self-supervised vision models for medical imaging via second-order graph matching

Large vision models for robust and trustworthy medical image understanding

Perspective