具有人口统计学意识的细粒度视觉识别用于儿童手腕病理学
Demographic-aware fine-grained visual recognition of pediatric wrist pathologies
AI总结：
本文提出一种结合患者年龄和性别的混合卷积-Transformer模型，用于细粒度视觉识别儿童手腕病理学，以提高诊断准确性。
摘要：
从放射影像中识别儿童手腕病理学具有挑战性，因为正常解剖结构随着发育迅速变化：进化中的腕骨骨化和开放生长时间可以模仿病理学，而成熟时间因性别而异。仅图像模型在有限的医疗数据集上训练因此可能将正常的发育变化混淆为真正的病理学。我们通过将儿童手腕诊断框架为细粒度视觉识别（FGVR）问题，并提出一个融合X光和患者年龄和性别的人口统计学意识混合卷积-变压器模型来解决这一问题。为了利用人口统计学上下文同时避免依赖捷径，我们在训练期间引入了渐进式元数据掩码。我们在一个经过精心编排的数据集上进行评估，该数据集反映了现实世界医学研究中的典型限制。混合FGVR主干比传统和现代CNNs表现更好，而人口统计学融合带来了额外的增益。最后，我们展示初始化来自细粒度预训练源可以提高转移相对于标准ImageNet初始化，表明即使从非医疗数据中获得的标签粒度也可以是驱动微妙放射影像发现泛化的关键因素。
英文摘要：
Pediatric wrist pathologies recognition from radiographs is challenging because normal anatomy changes rapidly with development: evolving carpal ossification and open physes can resemble pathology, and maturation timing differs by sex. Image-only models trained on limited medical datasets therefore risk confusing normal developmental variation with true pathologies. We address this by framing pediatric wrist diagnosis as a fine-grained visual recognition (FGVR) problem and proposing a demographic-aware hybrid convolution--transformer model that fuses X-rays with patient age and sex. To leverage demographic context while avoiding shortcut reliance, we introduce progressive metadata masking during training. We evaluate on a curated dataset that mirrors the typical constraints in real-world medical studies. The hybrid FGVR backbone outperforms traditional and modern CNNs, and demographic fusion yields additional gains. Finally, we show that initializing from a fine-grained pretraining source improves transfer relative to standard ImageNet initialization, suggesting that label granularity, even from non-medical data, can be a key driver of generalization for subtle radiographic findings.
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