  Medical image segmentation aims to identify and locate abnormal structures in
medical images, such as chest radiographs, using deep neural networks. These
networks require a large number of annotated images with fine-grained masks for
the regions of interest, making pre-training strategies based on classification
datasets essential for sample efficiency. Based on a large-scale medical image
classification dataset, our work collects explanations from well-trained
classifiers to generate pseudo labels of segmentation tasks. Specifically, we
offer a case study on chest radiographs and train image classifiers on the
CheXpert dataset to identify 14 pathological observations in radiology. We then
use Integrated Gradients (IG) method to distill and boost the explanations
obtained from the classifiers, generating massive diagnosis-oriented
localization labels (DoLL). These DoLL-annotated images are used for
pre-training the model before fine-tuning it for downstream segmentation tasks,
including COVID-19 infectious areas, lungs, heart, and clavicles. Our method
outperforms other baselines, showcasing significant advantages in model
performance and training efficiency across various segmentation settings.
