学习适应与校准：面向医学视觉语言模型少样本不确定性预测的分数分布对齐
Learning to Adapt and Calibrate: Score Distribution Alignment for Few-Shot Uncertainty Prediction in Medical VLMs
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中文总结 AI 辅助
针对少样本医学VLM中标准共形预测因监督适应破坏可交换性导致覆盖不可靠的问题，提出AlignCP框架，通过学习重加权校准分布对齐分数分布，在不需查询标签下缩小覆盖差距。
中文摘要 AI 辅助
利用共形预测对医学视觉语言模型（VLMs）进行不确定性估计，因其无分布覆盖保证而受到越来越多的关注。然而，标准共形预测依赖于校准数据与测试数据之间的可交换性，并且通常需要足够大的校准集才能获得可靠的覆盖。这些假设在少样本迁移设置中难以满足，在该设置中，仅有一个小的带标签支持集可用于将预训练的VLM适应到新的医学任务，而无标签的查询集用于评估。在支持集上进行监督微调会改变模型参数，从而改变非一致性分数的分布，破坏校准样本与查询样本之间的可交换性，并导致在分布偏移下覆盖不可靠。现有的转导式共形适应方法通常通过避免监督更新来保持有效性。虽然这有助于维持共形假设，但它未充分利用稀缺的带标签支持数据，并限制了任务适应，而任务适应正是少样本学习的主要目标。在这种设置下，共形预测应作为支持适应模型的不确定性估计层，而不是阻碍适应本身。为此，我们提出了AlignCP框架，该框架在非可交换性下调和了监督式少样本适应与共形不确定性估计。AlignCP学习一个重新加权的校准分布，以减少带标签支持集与无标签查询集之间的分数级差异。通过对齐一维非一致性分数分布，AlignCP旨在缩小由适应引起的覆盖差距，而无需查询标签。
英文摘要
Uncertainty estimation for medical vision--language models (VLMs) using conformal prediction has gained increasing attention due to its distribution-free coverage guarantees. However, standard conformal prediction relies on exchangeability between calibration and test data and typically requires a sufficiently large calibration set to obtain reliable coverage. These assumptions are difficult to satisfy in few-shot transfer settings, where only a small labeled support set is available to adapt a pretrained VLM to a new medical task, while an unlabeled query set is used for evaluation. Supervised fine-tuning on the support set changes the model parameters and consequently shifts the nonconformity score distribution, breaking exchangeability between calibration and query samples and leading to unreliable coverage under distribution shift. Existing transductive conformal adaptation methods often preserve validity by avoiding supervised updates. While this helps maintain conformal assumptions, it underutilizes the scarce labeled support data and limits task adaptation, which is the primary objective in few-shot learning. In this setting, conformal prediction should serve as an uncertainty estimation layer that supports the adapted model, rather than preventing adaptation itself. To this end, we propose AlignCP, a framework that reconciles supervised few-shot adaptation with conformal uncertainty estimation under non-exchangeability. AlignCP learns a reweighted calibration distribution that reduces the score-level discrepancy between the labeled support set and the unlabeled query set. By aligning the one-dimensional nonconformity score distributions, AlignCP aims to close the coverage gap induced by adaptation without requiring query labels.
发表机构
- University of Arkansas（阿肯色大学）
机构由 AI 辅助整理，请以论文原文为准。
