  Representation learning offers a conduit to elucidate distinctive features
within the latent space and interpret the deep models. However, the randomness
of lesion distribution and the complexity of low-quality factors in medical
images pose great challenges for models to extract key lesion features. Disease
diagnosis methods guided by contrastive learning (CL) have shown significant
advantages in lesion feature representation. Nevertheless, the effectiveness of
CL is highly dependent on the quality of the positive and negative sample
pairs. In this work, we propose a clinical-oriented multi-level CL framework
that aims to enhance the model's capacity to extract lesion features and
discriminate between lesion and low-quality factors, thereby enabling more
accurate disease diagnosis from low-quality medical images. Specifically, we
first construct multi-level positive and negative pairs to enhance the model's
comprehensive recognition capability of lesion features by integrating
information from different levels and qualities of medical images. Moreover, to
improve the quality of the learned lesion embeddings, we introduce a dynamic
hard sample mining method based on self-paced learning. The proposed CL
framework is validated on two public medical image datasets, EyeQ and Chest
X-ray, demonstrating superior performance compared to other state-of-the-art
disease diagnostic methods.
