Location embedding based pairwise distance learning for fine-grained diagnosis of urinary stones
This work addresses fine-grained diagnosis of urinary stones for medical imaging applications, representing an incremental advance with a novel method for a known bottleneck.
The paper tackled the problem of precise diagnosis of urinary stones, which is complicated by low contrast and location variability, by proposing LEPD-Net that leverages low-dose X-ray imaging and location information, achieving significant improvements over state-of-the-art methods on an in-house dataset.
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.