Virchow2: Scaling self-supervised mixed magnification models in pathology
Foundation models are rapidly being developed for computational pathology applications.
However, it remains an open question which factors are most important for downstream …
However, it remains an open question which factors are most important for downstream …
[HTML][HTML] Self-supervised visual learning in the low-data regime: a comparative evaluation
Abstract Self-Supervised Learning (SSL) is a valuable and robust training methodology for
contemporary Deep Neural Networks (DNNs), enabling unsupervised pretraining on a …
contemporary Deep Neural Networks (DNNs), enabling unsupervised pretraining on a …
TIPS: Text-Image Pretraining with Spatial Awareness
While image-text representation learning has become very popular in recent years, existing
models tend to lack spatial awareness and have limited direct applicability for dense …
models tend to lack spatial awareness and have limited direct applicability for dense …
Clustering Properties of Self-Supervised Learning
X Weng, J An, X Ma, B Qi, J Luo, X Yang… - arxiv preprint arxiv …, 2025 - arxiv.org
Self-supervised learning (SSL) methods via joint embedding architectures have proven
remarkably effective at capturing semantically rich representations with strong clustering …
remarkably effective at capturing semantically rich representations with strong clustering …