SOTAVerified

Breast Cancer Histology Image Classification

Papers

Showing 110 of 17 papers

TitleStatusHype
Breast-NET: a lightweight DCNN model for breast cancer detection and grading using histological samplesCode0
Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer VisionCode0
Classification of Breast Cancer Histopathology Images using a Modified Supervised Contrastive Learning MethodCode0
Rotation-Agnostic Image Representation Learning for Digital Pathology0
Attention-Map Augmentation for Hypercomplex Breast Cancer ClassificationCode0
Magnification Invariant Medical Image Analysis: A Comparison of Convolutional Networks, Vision Transformers, and Token Mixers0
BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pixCode2
VGGIN-Net: Deep Transfer Network for Imbalanced Breast Cancer DatasetCode1
Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological ImagesCode1
MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image ClassificationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1WaveMixAccuracy (%)99.39Unverified
2Breast-NETAccuracy (%)98.11Unverified
3VGGIN-NetAccuracy (%)96.15Unverified
4EfficientNet-b21:1 Accuracy92.23Unverified
5WaveMixLite-224/10Accuracy (%)91.72Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-152Accuracy (% )83Unverified