Vision Transformers (ViTs) are widely adopted in medical imaging tasks, and
some existing efforts have been directed towards vision-language training for
Chest X-rays (CXRs). However, we envision that there still exists a potential
for improvement in vision-only training for CXRs using ViTs, by aggregating
information from multiple scales, which has been proven beneficial for
non-transformer networks. Hence, we have developed LT-ViT, a transformer that
utilizes combined attention between image tokens and randomly initialized
auxiliary tokens that represent labels. Our experiments demonstrate that LT-ViT
(1) surpasses the state-of-the-art performance using pure ViTs on two publicly
available CXR datasets, (2) is generalizable to other pre-training methods and
therefore is agnostic to model initialization, and (3) enables model
interpretability without grad-cam and its variants.

利用 LT-ViT 来从多个尺度聚合信息，实现对胸部 X 光图像进行纯可视化模型训练，其优于基于纯 ViTs 的现有方法，在两个公开 CXR 数据集上表现出最先进的性能，并且对于其他预训练方法具有泛化性且不依赖于模型初始化，并且能够实现模型的可解释性而无需使用 grad-cam 及其变种。