Deep learning with noisy labels in medical prediction problems: a
  scoping review
Yishu Wei Yu Deng Cong Sun Mingquan Lin Hongmei Jiang Yifan Peng
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.