Understanding the impact of image and input resolution on deep digital pathology patch classifiers

@article{Teh2022UnderstandingTI,
  title={Understanding the impact of image and input resolution on deep digital pathology patch classifiers},
  author={Eu Wern Teh and Graham W. Taylor},
  journal={2022 19th Conference on Robots and Vision (CRV)},
  year={2022},
  pages={159-166}
}
We consider annotation efficient learning in Digital Pathology (DP), where expert annotations are expensive and thus scarce. We explore the impact of image and input resolution on DP patch classification performance. We use two cancer patch classification datasets PCam and CRC, to validate the results of our study. Our experiments show that patch classification performance can be improved by manipulating both the image and input resolution in annotation-scarce and annotation-rich environments… 

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