A new 321-patient multi-center breast FNAC WSI dataset with 7398 patch-level C1-C5 annotations is released for AI-assisted classification research.
A Dataset for Breast Cancer Histopathological Image Classification
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
roles
dataset 1polarities
use dataset 1representative citing papers
A hybrid Swin Transformer and ResNet50 transfer learning model achieves up to 100% test accuracy on multi-type cancer histopathological image classification.
A graph autoencoder model using foundation model features achieves high retrieval accuracy (mAP 96.7-97.6%, mMV 91.5-94.2%) on BreakHis and BACH breast cancer histopathology datasets.
citing papers explorer
-
A Multi Center Breast FNAC Whole-Slide Cytology Dataset for AI-Assisted Patch-Wise Classification Using C1 to C5 Reporting Categories
A new 321-patient multi-center breast FNAC WSI dataset with 7398 patch-level C1-C5 annotations is released for AI-assisted classification research.
-
Leveraging Medical Foundation Model Features in Graph Neural Network-Based Retrieval of Breast Histopathology Images
A graph autoencoder model using foundation model features achieves high retrieval accuracy (mAP 96.7-97.6%, mMV 91.5-94.2%) on BreakHis and BACH breast cancer histopathology datasets.