Foundation models have recently become a popular research direction within computational pathology. They are intended to be general-purpose feature extractors, promising to achieve good performance on a range of downstream tasks. Real-world pathology image data does however exhibit considerable variability. Foundation models should be robust to these variations and other distribution shifts which might be encountered in practice. We evaluate two computational pathology foundation models: UNI (trained on more than 100,000 whole-slide images) and CONCH (trained on more than 1.1 million image-caption pairs), by utilizing them as feature extractors within prostate cancer grading models. We find that while UNI and CONCH perform well relative to baselines, the absolute performance can still be far from satisfactory in certain settings. The fact that foundation models have been trained on large and varied datasets does not guarantee that downstream models always will be robust to common distribution shifts.

本研究解决了计算病理学中基础模型面对真实病理图像数据的变异性和分布偏移的鲁棒性问题。通过评估两种基础模型（UNI和CONCH）在前列腺癌分级模型中的应用，本文揭示了尽管这两种模型相对基线表现良好，但在某些情况下，绝对表现仍然不令人满意。这一发现突显了大规模和多样化数据集训练的基础模型在实际应用中的局限性。

评估计算病理基础模型在前列腺癌分级中的表现及其在分布偏移下的影响