CoFailMed 审计：11 个预训练编码器在医学分割中的相关性失效研究
Different Models, Shared Failures: Auditing Frozen-Encoder Pools for Medical Segmentation
这篇论文用 11 个编码器证明：多模型投票可能只是假冗余，四个模型有效规模不到三个。做医学分割集成的人该看看。
arXiv 论文提出 CoFailMed，对医学图像分割中多个预训练编码器池的相关性失效进行审计。研究在五个 2D 分割任务上评估 11 个公开预训练编码器，使用冻结特征配合单独训练的 1×1 readout，每个编码器跑 4 个随机种子。结果显示共享 checkpoint 的模型对平均每例 Dice 相关性最高，且同一编码器家族内的不同 checkpoint 之间相关性仍高于跨家族组合。在池化 33 百分位 Dice 阈值下，四个不同编码器组成的池平均有效规模仅 1.56-2.92，说明名义上的多模型组合实际冗余很大。论文同时发布逐例对齐结果、分析脚本与研究代码以复现核心审计结果。
Different Models, Shared Failures: Auditing Frozen-Encoder Pools for Medical Segmentation
Average segmentation accuracy and model count do not reveal whether models fail on the same cases. We present CoFailMed, an audit of correlated failures in pretrained-encoder pools for medical segmentation. Across five 2D tasks, we evaluate 11 public pretrained encoders (a controlled sample, not a census) using frozen features and separately trained $1\times 1$ readouts with four seeds per encoder. Shared-checkpoint pairs have the highest mean per-case Dice correlation. An expanded 17-encoder analysis shows that distinct checkpoints within the same encoder family remain more correlated than cross-family pairs. At the pooled 33rd-percentile Dice threshold, four-member pools drawn from distinct encoders have mean effective sizes of 1.56-2.92, estimated from failure correlations in pools with enough failures and passes per member. In the primary pool, richer readouts and fine-tuning narrow the Dice-correlation gap between pairs sharing a checkpoint (or the DINOv2-B/S release) and all other pairs. Yet dependence between different encoders can increase, and fine-tuning does not consistently increase effective pool size at 33rd-percentile thresholds recomputed for each training recipe. A smaller between-group gap can coexist with greater dependence. Pool evaluation should therefore report absolute dependence and threshold-specific effective size alongside accuracy and nominal pool size. The CoFailMed release provides aligned per-case traces, analysis scripts and study code to reproduce the central audit results.