Reliability-Aware Benchmarking of Self-Supervised Learning in Histopathology Under 1-10% Label Regimes

원문 초록

Expert annotation in digital pathology is expensive, and low-label regimes remain common in realistic histopathology workflows. Self-supervised learning (SSL) offers a practical way to learn transferable representations from unlabeled pathology images, but performance-only evaluation can hide unreliable confidence behavior under domain shift. We present a reliability-aware benchmark of SimCLR, BYOL, and an MAE-inspired masked-reconstruction baseline on PatchCamelyon (PCam; 96x96) and PANDA (prostate cancer) under 1%, 5%, and 10% labeled regimes with frozen-encoder linear probing. To address common single-run concerns, ResNet-18 results are repeated over 3 splits x 3 seeds (n=9) and ViT-B/16 over 1 split x 3 seeds (n=3) under practical compute constraints, with mean+/-std reporting and per-run logs. Beyond AUROC, accuracy, and F1-score, we report Expected Calibration Error and Brier score, OOD confidence diagnostics for cross-dataset PCam<->PANDA scoring, federated-vs-centralized SimCLR comparisons on PCam, and cross-dataset transfer to colorectal histology tiles. SSL improves label efficiency on the more controlled PCam task, while PANDA's noisier patch-level proxy can remain near coin-flip with higher variance. The study highlights that histopathology SSL should be assessed not only by ranking performance but also by calibration, robustness, and transparent reproducibility diagnostics before clinical interpretation.