标题： 伪D：通过校准的神经训练动力学指导多视图不确定性估计 显示英文标题

标题： Pseudo-D: Informing Multi-View Uncertainty Estimation with Calibrated Neural Training Dynamics

摘要： 计算机辅助诊断系统必须从医学图像中做出关键决策，这些图像通常存在噪声、模糊或矛盾，但目前的模型是在过于简化的标签上进行训练的，忽略了诊断的不确定性。 独热标签消除了评分者之间的差异，并迫使模型在面对不完整或含有伪影的输入时做出过于自信的预测。 我们通过引入一种新框架来弥补这一差距，将不确定性重新引入标签空间。 我们的方法利用神经网络训练动力学（NNTD）来评估每个训练样本的固有难度。 通过在训练期间聚合和校准模型预测，我们生成具有不确定性的伪标签，这些标签反映了学习过程中遇到的模糊性。 这种标签增强方法与架构无关，可以应用于任何监督学习流程，以提高不确定性估计和鲁棒性。 我们在一个具有挑战性的超声心动图分类基准上验证了我们的方法，在校准、选择性分类和多视角融合方面，其性能优于专门的基线方法。 显示英文摘要

摘要： Computer-aided diagnosis systems must make critical decisions from medical images that are often noisy, ambiguous, or conflicting, yet today's models are trained on overly simplistic labels that ignore diagnostic uncertainty. One-hot labels erase inter-rater variability and force models to make overconfident predictions, especially when faced with incomplete or artifact-laden inputs. We address this gap by introducing a novel framework that brings uncertainty back into the label space. Our method leverages neural network training dynamics (NNTD) to assess the inherent difficulty of each training sample. By aggregating and calibrating model predictions during training, we generate uncertainty-aware pseudo-labels that reflect the ambiguity encountered during learning. This label augmentation approach is architecture-agnostic and can be applied to any supervised learning pipeline to enhance uncertainty estimation and robustness. We validate our approach on a challenging echocardiography classification benchmark, demonstrating superior performance over specialized baselines in calibration, selective classification, and multi-view fusion.