VTAB is a 19-task benchmark that measures representation quality by few-shot adaptation performance across diverse vision domains, with a controlled large-scale comparison of popular pretraining methods.
Detecting cancer metastases on gigapixel pathol- ogy images
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
A context-aware CNN using 1792x1792 images and spatial feature aggregation outperforms patch-based methods for colorectal cancer grading by 3.61%.
MIDOG 2025 challenge shows top mitosis detection F1 of 0.740 and atypical figure balanced accuracy of 0.908 across diverse tumors, with clear drops in challenging regions and tumor-type variation.
A CNN model trained with pseudo-label semi-supervised learning reports higher AUC than a supervised baseline on the PCam histopathology dataset.
citing papers explorer
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A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
VTAB is a 19-task benchmark that measures representation quality by few-shot adaptation performance across diverse vision domains, with a controlled large-scale comparison of popular pretraining methods.
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Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images
A context-aware CNN using 1792x1792 images and spatial feature aggregation outperforms patch-based methods for colorectal cancer grading by 3.61%.
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Mitosis Detection in the Wild: Multi-Tumor and Context-Aware Generalization in the MIDOG 2025 Challenge
MIDOG 2025 challenge shows top mitosis detection F1 of 0.740 and atypical figure balanced accuracy of 0.908 across diverse tumors, with clear drops in challenging regions and tumor-type variation.
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Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases
A CNN model trained with pseudo-label semi-supervised learning reports higher AUC than a supervised baseline on the PCam histopathology dataset.