Numerous deep learning-based solutions have been proposed for histopathological image analysis over the past years. While they usually demonstrate exceptionally high accuracy, one key question is whether their precision might be affected by low-level image properties not related to histopathology but caused by microscopy image handling and pre-processing. In this paper, we analyze a popular NCT-CRC-HE-100K colorectal cancer dataset used in numerous prior works and show that both this dataset and the obtained results may be affected by data-specific biases. The most prominent revealed dataset issues are inappropriate color normalization, severe JPEG artifacts inconsistent between different classes, and completely corrupted tissue samples resulting from incorrect image dynamic range handling. We show that even the simplest model using only 3 features per image (red, green and blue color intensities) can demonstrate over 50% accuracy on this 9-class dataset, while using color histogram not explicitly capturing cell morphology features yields over 82% accuracy. Moreover, we show that a basic EfficientNet-B0 ImageNet pretrained model can achieve over 97.7% accuracy on this dataset, outperforming all previously proposed solutions developed for this task, including dedicated foundation histopathological models and large cell morphology-aware neural networks. The NCT-CRC-HE dataset is publicly available and can be freely used to replicate the presented results. The codes and pre-trained models used in this paper are available at https://github.com/gmalivenko/NCT-CRC-HE-experiments

本研究探讨了在组织病理图像分析中广泛应用的NCT-CRC-HE-100K结肠癌数据集，揭示了由于不当的颜色标准化和JPEG压缩伪影等问题，数据集结果可能受到特定偏差的影响。通过实验表明，简单模型的准确率可超过50%，而基础高效网络模型甚至能达到97.7%的准确率，显著超越了以往的方法，突显了数据集的潜在问题和研究的风险。

NCT-CRC-HE: 并非所有的组织病理学数据集都同样有用