Objectives: Medical research faces substantial challenges from noisy labels
attributed to factors like inter-expert variability and machine-extracted
labels. Despite this, the adoption of label noise management remains limited,
and label noise is largely ignored. To this end, there is a critical need to
conduct a scoping review focusing on the problem space. This scoping review
aims to comprehensively review label noise management in deep learning-based
medical prediction problems, which includes label noise detection, label noise
handling, and evaluation. Research involving label uncertainty is also
included.
Methods: Our scoping review follows the Preferred Reporting Items for
Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched 4
databases, including PubMed, IEEE Xplore, Google Scholar, and Semantic Scholar.
Our search terms include "noisy label AND medical / healthcare / clinical",
"un-certainty AND medical / healthcare / clinical", and "noise AND medical /
healthcare / clinical".
Results: A total of 60 papers met inclusion criteria between 2016 and 2023. A
series of practical questions in medical research are investigated. These
include the sources of label noise, the impact of label noise, the detection of
label noise, label noise handling techniques, and their evaluation.
Categorization of both label noise detection methods and handling techniques
are provided.
Discussion: From a methodological perspective, we observe that the medical
community has been up to date with the broader deep-learning community, given
that most techniques have been evaluated on medical data. We recommend
considering label noise as a standard element in medical research, even if it
is not dedicated to handling noisy labels. Initial experiments can start with
easy-to-implement methods, such as noise-robust loss functions, weighting, and
curriculum learning.

通过对深度学习的医学预测问题中的标签噪声管理进行全面审查，包括标签噪声检测、标签噪声处理和评估，本文提供了一系列实用问题的研究结果，其中包括标签噪声的来源、影响、检测、处理技术以及它们的评估，还提供了标签噪声检测方法和处理技术的分类。

医学预测问题中的有噪标签深度学习：一项范围审查

Deep learning with noisy labels in medical prediction problems: a  scoping review

Convolutional Neural Networks (CNNs) are frequently and successfully used in
medical prediction tasks. They are often used in combination with transfer
learning, leading to improved performance when training data for the task are
scarce. The resulting models are highly complex and typically do not provide
any insight into their predictive mechanisms, motivating the field of
'explainable' artificial intelligence (XAI). However, previous studies have
rarely quantitatively evaluated the 'explanation performance' of XAI methods
against ground-truth data, and transfer learning and its influence on objective
measures of explanation performance has not been investigated. Here, we propose
a benchmark dataset that allows for quantifying explanation performance in a
realistic magnetic resonance imaging (MRI) classification task. We employ this
benchmark to understand the influence of transfer learning on the quality of
explanations. Experimental results show that popular XAI methods applied to the
same underlying model differ vastly in performance, even when considering only
correctly classified examples. We further observe that explanation performance
strongly depends on the task used for pre-training and the number of CNN layers
pre-trained. These results hold after correcting for a substantial correlation
between explanation and classification performance.

本研究提出了针对磁共振成像分类任务的基准数据集，并利用该数据集了解迁移学习对解释质量的影响。实验结果表明，应用于相同基础模型的流行的 XAI 方法在性能上有很大差异，即使只考虑分类正确的例子。我们进一步观察到，解释性能强烈依赖于用于预训练的任务和预训练的 CNN 层数。

基准数据用于研究预训练对磁共振图像分类解释性能的影响

Benchmark data to study the influence of pre-training on explanation  performance in MR image classification

Health conditions among patients in intensive care units (ICUs) are monitored
via electronic health records (EHRs), composed of numerical time series and
lengthy clinical note sequences, both taken at irregular time intervals.
Dealing with such irregularity in every modality, and integrating irregularity
into multimodal representations to improve medical predictions, is a
challenging problem. In this paper, we address this problem by (1) modeling
irregular time series by incorporating hand-crafted imputation embeddings into
learned interpolation embeddings via a gating mechanism; (2) casting a series
of clinical note representations as multivariate irregular time series and
tackling irregularity via a time attention mechanism; and (3) fusing
multimodalities with an interleaved attention mechanism across temporal steps
to integrate irregularity into multimodal representations. To the best of our
knowledge, this is the first work to thoroughly model irregularity in
multimodalities and to take into account temporal knowledge in multimodal
fusion, for improving medical predictions. The results on two medical
prediction tasks show that our proposed methods outperform the state-of-the-art
(SOTA) methods in both every single modality and multimodal fusion scenarios,
illustrating the effectiveness of our methods and the value of modeling
irregularity in multimodal fusion.

本论文通过手工建模和学习内插嵌入，采用分离机制建模不规则的时间序列；采用时间注意力机制，将临床记录的一系列表示为多元不规则时间序列，并采用交织注意机制跨时间步骤进行多模态融合，以整合多模态表示。结果表明，我们提出的方法在单一模态和多模态情景下都优于现有方法，说明了我们方法的有效性和模拟不规则性的价值。