TOAD
Tumor Origin Assessment via Deep-learning; weakly-supervised multi-task model predicting cancer primary origin from H&E whole-slide images.
- from
- Computational Biology
- added
- 2026-10-10
- likes
- 0
similar
-
UNI github.com
General-purpose self-supervised pathology foundation model trained on 100K+ whole-slide images for diverse computational pathology tasks.
-
PORPOISE github.com
Pan-cancer integrative histology-genomic analysis framework using multimodal deep learning for patient stratification.
-
TESSERA github.com
Cancer-genome foundation model jointly pretrained on somatic SNVs and copy-number alterations from TCGA using masked reconstruction and cross-modal contrastive learning.
-
DeepSpot github.com
Deep learning model predicting spatial transcriptomics from H&E images at spot and single-cell resolution.
-
TITAN github.com
Multimodal whole-slide pathology foundation model that combines image and language supervision for slide-level representation and zero-shot analysis.
-
Computational Biology › Machine Learning Tasks and Models > Foundation Models > Multi-Modal Foundation Models: “Tumor Origin Assessment via Deep-learning; weakly-supervised multi-task model predicting cancer primary origin from H&E whole-slide images.”