Robust Asymmetric Loss for Multi-Label Long-Tailed Learning
Wongi Park Inhyuk Park Sungeun Kim Jongbin Ryu
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: https://github.com/kalelpark/RAL.