In real medical data, training samples typically show long-tailed
distributions with multiple labels. Class distribution of the medical data has
a long-tailed shape, in which the incidence of different diseases is quite
varied, and at the same time, it is not unusual for images taken from
symptomatic patients to be multi-label diseases. Therefore, in this paper, we
concurrently address these two issues by putting forth a robust asymmetric loss
on the polynomial function. Since our loss tackles both long-tailed and
multi-label classification problems simultaneously, it leads to a complex
design of the loss function with a large number of hyper-parameters. Although a
model can be highly fine-tuned due to a large number of hyper-parameters, it is
difficult to optimize all hyper-parameters at the same time, and there might be
a risk of overfitting a model. Therefore, we regularize the loss function using
the Hill loss approach, which is beneficial to be less sensitive against the
numerous hyper-parameters so that it reduces the risk of overfitting the model.
For this reason, the proposed loss is a generic method that can be applied to
most medical image classification tasks and does not make the training process
more time-consuming. We demonstrate that the proposed robust asymmetric loss
performs favorably against the long-tailed with multi-label medical image
classification in addition to the various long-tailed single-label datasets.
Notably, our method achieves Top-5 results on the CXR-LT dataset of the ICCV
CVAMD 2023 competition. We opensource our implementation of the robust
asymmetric loss in the public repository: this https URL

本文提出了一种鲁棒的不对称损失函数，能够同时解决长尾和多标签分类问题，适用于医学图像分类任务，能够有效缓解多个超参数优化困难和模型过拟合的风险。实验证明该方法在长尾多标签医学图像分类和其他长尾单标签数据集上具有较好的性能，且在 ICCV CVAMD 2023 竞赛的 CXR-LT 数据集上取得 Top-5 的结果。

多标签长尾学习的鲁棒非对称损失

Robust Asymmetric Loss for Multi-Label Long-Tailed Learning

Training NLP systems typically assumes access to annotated data that has a
single human label per example. Given imperfect labeling from annotators and
inherent ambiguity of language, we hypothesize that single label is not
sufficient to learn the spectrum of language interpretation. We explore new
annotation distribution schemes, assigning multiple labels per example for a
small subset of training examples. Introducing such multi label examples at the
cost of annotating fewer examples brings clear gains on natural language
inference task and entity typing task, even when we simply first train with a
single label data and then fine tune with multi label examples. Extending a
MixUp data augmentation framework, we propose a learning algorithm that can
learn from training examples with different amount of annotation (with zero,
one, or multiple labels). This algorithm efficiently combines signals from
uneven training data and brings additional gains in low annotation budget and
cross domain settings. Together, our method achieves consistent gains in two
tasks, suggesting distributing labels unevenly among training examples can be
beneficial for many NLP tasks.

该研究论文通过在一小部分训练样例中为每个样例分配多个标签的方法，提出可通过利用不同标注数量的训练样例，设计高效的学习算法，提高自然语言处理的任务表现。

不同标注数量下的学习：从零到多标签

Learning with Different Amounts of Annotation: From Zero to Many Labels

Crowd sourcing has become a widely adopted scheme to collect ground truth
labels. However, it is a well-known problem that these labels can be very
noisy. In this paper, we demonstrate how to learn a deep convolutional neural
network (DCNN) from noisy labels, using facial expression recognition as an
example. More specifically, we have 10 taggers to label each input image, and
compare four different approaches to utilizing the multiple labels: majority
voting, multi-label learning, probabilistic label drawing, and cross-entropy
loss. We show that the traditional majority voting scheme does not perform as
well as the last two approaches that fully leverage the label distribution. An
enhanced FER+ data set with multiple labels for each face image will also be
shared with the research community.

本文演示如何使用深度卷积神经网络 (DCNN) 从嘈杂的标签中学习，并以面部表情识别为例。通过比较多数投票，多标签学习，概率标签绘制和交叉熵损失四种方法来利用多个标签，我们发现传统的多数投票方案表现不如完全利用标签分布的最后两种方法。此外，我们还向研究社区共享了一组多标签面部图像数据集。