- RudolfV: RudolfV: A Foundation Model by Pathologists for Pathologists
- Prov-GigaPath: A whole-slide foundation model for digital pathology from real-world data
- UNI: Towards a general-purpose foundation model for computational pathology
- Virchow: Virchow: A Million-Slide Digital Pathology Foundation Model
- Phikon: Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling
- 3B-CPath: Computational Pathology at Health System Scale – Self-Supervised Foundation Models from Three Billion Images
- CTransPath: Transformer-based unsupervised contrastive learning for histopathological image classification
- GTP: A Graph-Transformer for Whole Slide Image Classification
- H2-MIL: H2-Exploring hierarchical representation with heterogeneous multiple instance learning for whole slide image analysis.
- SET-MIL: SETMIL: Spatial Encoding Transformer-Based Multiple Instance Learning for Pathological Image Analysis
- DT-MIL: DT-MIL: deformable transformer for multi-instance learning on histopathological image
- HIPT: Scaling vision transformers to gigapixel images via hierarchical self-supervised learning
- DS-MIL: Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning
- TransMIL: Transmil: Transformer based correlated multiple instance learning for whole slide image classification
- CLAM: Data-efficient and weakly supervised computational pathology on whole-slide images
- VarMIL: Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling
- PatchGCN: Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks
- SlideGraph: Whole Slide Image Level Graphs to Predict HER2 Status in Breast Cancer
- LossAB-MIL: Loss-Based Attention for Deep Multiple Instance Learning
- DeepAttnMISL: Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks
- AttPool: AttPool:Towards Hierarchical Feature Representation in Graph Convolutional Networks via Attention Mechanism
- DeepMISL: Deep Multi-instance Learning for Survival Prediction from Whole Slide Images
- MIL-RNN: https://www.nature.com/articles/s41591-019-0508-1
- AB-MIL: Attention-based deep multiple instance learning
- DeepGraphConv: Graph CNN for Survival Analysis on Whole Slide Pathological Images
- Deep Sets: Deep Sets