Improving calibration performance in deep learning (DL) classification models
is important when planning the use of DL in a decision-support setting. In such
a scenario, a confident wrong prediction could lead to a lack of trust and/or
harm in a high-risk application. We evaluate the impact on accuracy and
calibration of two types of approach that aim to improve DL classification
model calibration: deterministic uncertainty methods (DUM) and
uncertainty-aware training. Specifically, we test the performance of three DUMs
and two uncertainty-aware training approaches as well as their combinations. To
evaluate their utility, we use two realistic clinical applications from the
field of cardiac imaging: artefact detection from phase contrast cardiac
magnetic resonance (CMR) and disease diagnosis from the public ACDC CMR
dataset. Our results indicate that both DUMs and uncertainty-aware training can
improve both accuracy and calibration in both of our applications, with DUMs
generally offering the best improvements. We also investigate the combination
of the two approaches, resulting in a novel deterministic uncertainty-aware
training approach. This provides further improvements for some combinations of
DUMs and uncertainty-aware training approaches.

通过评估两种方法对深度学习分类模型的影响，即确定性不确定性方法和不确定性感知培训，我们发现这两种方法都可以在心脏成像领域的两个实际临床应用中提高准确性和校准度，其中确定性不确定性方法一般提供最佳改进效果，同时它们的组合也进一步改进了部分确定性不确定性方法和不确定性感知培训方法的组合。

利用确定性不确定性网络和不确定性感知训练改进心脏应用的深度学习模型校准

Improving Deep Learning Model Calibration for Cardiac Applications using  Deterministic Uncertainty Networks and Uncertainty-aware Training

Predictive uncertainty estimation is essential for deploying Deep Neural
Networks in real-world autonomous systems. However, most successful approaches
are computationally intensive. In this work, we attempt to address these
challenges in the context of autonomous driving perception tasks. Recently
proposed Deterministic Uncertainty Methods (DUM) can only partially meet such
requirements as their scalability to complex computer vision tasks is not
obvious. In this work we advance a scalable and effective DUM for
high-resolution semantic segmentation, that relaxes the Lipschitz constraint
typically hindering practicality of such architectures. We learn a discriminant
latent space by leveraging a distinction maximization layer over an
arbitrarily-sized set of trainable prototypes. Our approach achieves
competitive results over Deep Ensembles, the state-of-the-art for uncertainty
prediction, on image classification, segmentation and monocular depth
estimation tasks. Our code is available at this https URL

该研究提出了一种可扩展的、有效的决策不确定性方法，通过利用区别最大化层，通过任意大小的可训练原型学习鉴别隐藏空间，以解决深度学习在实际中应用时不确定性预测的问题，该方法在图像分类、分割和单眼深度估计任务上超过了深度集合的现有技术。

潜在判别确定性不确定性

Latent Discriminant deterministic Uncertainty

A set of novel approaches for estimating epistemic uncertainty in deep neural
networks with a single forward pass has recently emerged as a valid alternative
to Bayesian Neural Networks. On the premise of informative representations,
these deterministic uncertainty methods (DUMs) achieve strong performance on
detecting out-of-distribution (OOD) data while adding negligible computational
costs at inference time. However, it remains unclear whether DUMs are well
calibrated and can seamlessly scale to real-world applications - both
prerequisites for their practical deployment. To this end, we first provide a
taxonomy of DUMs, and evaluate their calibration under continuous
distributional shifts. Then, we extend them to semantic segmentation. We find
that, while DUMs scale to realistic vision tasks and perform well on OOD
detection, the practicality of current methods is undermined by poor
calibration under distributional shifts.

本文提出了一组新的方法，可以通过单次前向传递来估计深度神经网络中的认识不确定性，这些确定性不确定性方法在表示信息的前提下，在检测超出分布数据（OOD）方面表现出强大的性能，同时几乎不增加推理时间的计算成本。然而，DUMs 是否校准良好且是否可以无缝地扩展到实际应用仍不清楚。本文对 DUM 进行分类和评估，发现当前方法的实用性受到分布移位校准的限制。