A hybrid Swin Transformer and ResNet50 transfer learning model achieves up to 100% test accuracy on multi-type cancer histopathological image classification.
A Dataset for Breast Cancer Histopathological Image Classification
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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.
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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.