  Deep learning models were frequently reported to learn from shortcuts like
dataset biases. As deep learning is playing an increasingly important role in
the modern healthcare system, it is of great need to combat shortcut learning
in medical data as well as develop unbiased and trustworthy models. In this
paper, we study the problem of developing debiased chest X-ray diagnosis models
from the biased training data without knowing exactly the bias labels. We start
with the observations that the imbalance of bias distribution is one of the key
reasons causing shortcut learning, and the dataset biases are preferred by the
model if they were easier to be learned than the intended features. Based on
these observations, we proposed a novel algorithm, pseudo bias-balanced
learning, which first captures and predicts per-sample bias labels via
generalized cross entropy loss and then trains a debiased model using pseudo
bias labels and bias-balanced softmax function. We constructed several chest
X-ray datasets with various dataset bias situations and demonstrated with
extensive experiments that our proposed method achieved consistent improvements
over other state-of-the-art approaches.
