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.
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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.
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DSVTLA: Deep Swin Vision Transformer-Based Transfer Learning Architecture for Multi-Type Cancer Histopathological Cancer Image Classification
A hybrid Swin Transformer and ResNet50 transfer learning model achieves up to 100% test accuracy on multi-type cancer histopathological image classification.