  Clinical classification of chest radiography is particularly challenging for
standard machine learning algorithms due to its inherent long-tailed and
multi-label nature. However, few attempts take into account the coupled
challenges posed by both the class imbalance and label co-occurrence, which
hinders their value to boost the diagnosis on chest X-rays (CXRs) in the
real-world scenarios. Besides, with the prevalence of pretraining techniques,
how to incorporate these new paradigms into the current framework lacks of the
systematical study. This technical report presents a brief description of our
solution in the ICCV CVAMD 2023 CXR-LT Competition. We empirically explored the
effectiveness for CXR diagnosis with the integration of several advanced
designs about data augmentation, feature extractor, classifier design, loss
function reweighting, exogenous data replenishment, etc. In addition, we
improve the performance through simple test-time data augmentation and
ensemble. Our framework finally achieves 0.349 mAP on the competition test set,
ranking in the top five.
