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Breast Tumour Classification On Pcam

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Paper TitleRepository
Virchow0.933Virchow: A Million-Slide Digital Pathology Foundation Model
Steerable G-CNN (e)-Learning Steerable Filters for Rotation Equivariant CNNs-
VF-CNN (C8)-Rotation equivariant vector field networks
ResNet-34 (e)-Deep Residual Learning for Image Recognition
G-CNN (C4)-Group Equivariant Convolutional Networks
DenseNet-121 (e)-Densely Connected Convolutional Networks
G-CNN (C8)-Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis
VF-CNN (C12)-Rotation equivariant vector field networks
VF-CNN (C4)-Rotation equivariant vector field networks
Steerable G-CNN (C8)-Learning Steerable Filters for Rotation Equivariant CNNs-
ResNet-50 (e)-Deep Residual Learning for Image Recognition
p4m-DenseNet (D4)-Rotation Equivariant CNNs for Digital Pathology
Steerable G-CNN (C8)-Learning Steerable Filters for Rotation Equivariant CNNs-
Steerable G-CNN (C12)-Learning Steerable Filters for Rotation Equivariant CNNs-
G-CNN (C12)-Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis
DSF-CNN (C8)-Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images
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Breast Tumour Classification On Pcam | SOTA | HyperAI