Learning Where to Focus: Self-Supervised Multi-Scale ViTs for Histopathology
cs.CV
Submitted: 2026-09-16
Updated: 2026-09-16
Terminology
Sources
- Key Patches Are All You Need: A Multiple Instance Learning Framework For Robust Medical Diagnosis
- Token Merging: Your ViT But Faster
- Computational Pathology at Health System Scale -- Self-Supervised Foundation Models from Three Billion Images
- Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning
- A General-Purpose Self-Supervised Model for Computational Pathology
- Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology
- An Empirical Study of Training Self-Supervised Vision Transformers
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- Distilling foundation models for robust and efficient models in digital pathology
- RankMe: Assessing the downstream performance of pretrained self-supervised representations by their rank
- Attention-based Deep Multiple Instance Learning
- PLUTO: Pathology-Universal Transformer
- Benchmarking Self-Supervised Learning on Diverse Pathology Datasets
- Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations
- Self-Supervision Enhances Instance-based Multiple Instance Learning Methods in Digital Pathology: A Benchmark Study
- Representation Learning with Contrastive Predictive Coding
- DINOv2: Learning Robust Visual Features without Supervision
- Learning Transferable Visual Models From Natural Language Supervision
- ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification
- How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
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