Breast Tumour Classification On Pcam
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Accuracy |
|---|---|
| virchow-a-million-slide-digital-pathology | 0.933 |
| learning-steerable-filters-for-rotation | - |
| rotation-equivariant-vector-field-networks | - |
| deep-residual-learning-for-image-recognition | - |
| group-equivariant-convolutional-networks | - |
| densely-connected-convolutional-networks | - |
| roto-translation-equivariant-convolutional | - |
| rotation-equivariant-vector-field-networks | - |
| rotation-equivariant-vector-field-networks | - |
| learning-steerable-filters-for-rotation | - |
| deep-residual-learning-for-image-recognition | - |
| rotation-equivariant-cnns-for-digital | - |
| learning-steerable-filters-for-rotation | - |
| learning-steerable-filters-for-rotation | - |
| roto-translation-equivariant-convolutional | - |
| dense-steerable-filter-cnns-for-exploiting | - |