Location embedding based pairwise distance learning for fine-grained diagnosis of urinary stones

TL;DR

This paper introduces LEPD-Net, a novel deep learning framework that uses location embeddings and pairwise distance learning to improve the fine-grained diagnosis of urinary stones from low-dose X-ray images, addressing low contrast and variability issues.

Contribution

The paper proposes a new location embedding based pairwise distance learning network that enhances urinary stone diagnosis by integrating location information and fine-grained feature recognition.

Findings

LEPD-Net outperforms existing methods on the in-house dataset.

Location embedding improves stone detection accuracy.

Fine-grained pairwise distance learning enhances recognition of subtle differences.

Abstract

The precise diagnosis of urinary stones is crucial for devising effective treatment strategies. The diagnostic process, however, is often complicated by the low contrast between stones and surrounding tissues, as well as the variability in stone locations across different patients. To address this issue, we propose a novel location embedding based pairwise distance learning network (LEPD-Net) that leverages low-dose abdominal X-ray imaging combined with location information for the fine-grained diagnosis of urinary stones. LEPD-Net enhances the representation of stone-related features through context-aware region enhancement, incorporates critical location knowledge via stone location embedding, and achieves recognition of fine-grained objects with our innovative fine-grained pairwise distance learning. Additionally, we have established an in-house dataset on urinary tract stones to demonstrate the effectiveness of our proposed approach. Comprehensive experiments conducted on this dataset reveal that our framework significantly surpasses existing state-of-the-art methods.