CAMELYON+ BENCHMARK

INTRODUCTION

why we do this work?

Multiple Instance Learning (MIL) methods are mainstream approaches for pathological image classification and analysis. The CAMELYON-16/17 datasets are commonly used to evaluate MIL methods. However, they have the following issues:

• CAMELYON-16/17 datasets contain some problematic slides
• Pixel-annotations of CAMELYON-16/17 test-dataset not accurate enough
• Different MIL methods do not have a unified dataset-split and evaluation-metrics on the CAMELYON dataset
• To conclude,there is no BENCHMARK for MIL methods

what we do in this work?

We do the following work to establish a CAMELYON+ BENCHMARK

• Remove some problematic slides.
• Correct problematic annotations.
• Merge the correct version of**CAMELYON-16/17** datasets as the CAMELYON+ dataset.
• Evaluate mainstream MIL methods on the CAMELYON-NEW dataset.
• Evaluate mainstream feature extractors on the CAMELYON-NEW dataset.
• Use more comprehensive evaluation metrics to assess different methods.
• In summary, we establish a new CAMELYON+ BENCHMARK.

CAMELYON+

arxiv link

• ARXIV

dataset download

• SCIENCEDB

code available

• MIL_BASELINE