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
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2019 3representative citing papers
A context-aware CNN using 1792x1792 images and spatial feature aggregation outperforms patch-based methods for colorectal cancer grading by 3.61%.
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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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.