发表机构
Center for Computational & Data Sciences, Independent University, Bangladesh; Department of Computer Science and Engineering, Independent University, Bangladesh（孟加拉国独立大学计算与数据科学中心; 孟加拉国独立大学计算机科学与工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对女性健康盆腔成像数据匮乏问题，提出模态无关混合网络PelviNeXt，在PCOSGen和PXR150数据集上取得优异性能，为该领域提供可靠基础。
AI 中文摘要
女性健康在医学影像研究领域的资源仍严重不足，尽管多囊卵巢综合征（PCOS）、盆腔骨折等盆腔疾病具有重要临床意义，但公共的标注完善的基准数据却十分匮乏。我们提出PelviNeXt，这是一种模态无关的混合架构，结合了密集卷积特征提取器、分层通道-空间注意力（H-CBAM）、多尺度融合模块（MSFM）以及多头对话式自注意力（TH-MHSA），可不经修改直接应用于盆腔超声和X射线两种输入模态。在对唯一由妇科医生标注的公共PCOS超声数据集PCOSGen进行PelviNeXt基准测试时，我们发现该数据集内部及跨数据集存在大量精确和近重复污染。我们通过感知哈希对该污染进行审计，公开发布了去重后的数据集，并建立了首个经完整性审计的PCOSGen评估协议及基线，采用5折交叉验证。在唯一的公共盆腔骨折X射线数据集PXR150上，PelviNeXt在准确率、召回率、特异性和AUROC指标上均超过此前报道的最优结果。 ablation研究证实，各架构组件对两项任务的性能均有贡献。我们的结果表明，单一架构无需针对特定任务修改，即可作为女性健康领域数据稀缺、研究不足区域盆腔成像的可靠基础。
英文摘要
Women's health remains substantially under-resourced in medical imaging research, with pelvic pathologies such as polycystic ovary syndrome (PCOS) and pelvic fracture both suffering from a scarcity of public, well-annotated benchmark data despite their clinical importance. We introduce PelviNeXt, a modality-agnostic hybrid architecture combining a dense convolutional feature extractor, hierarchical channel-spatial attention (H-CBAM), a multi-scale fusion module (MSFM), and talking-heads multi-head self-attention (TH-MHSA), applied without modification to both pelvic ultrasound and X-ray inputs. While benchmarking PelviNeXt on PCOSGen, the only gynaecologist-annotated public PCOS ultrasound dataset, we identified extensive exact and near-duplicate contamination within and across the dataset. We audit this contamination via perceptual hashing, publicly release a deduplicated version of the dataset, and establish the first integrity-audited evaluation protocol and baseline for PCOSGen under 5-fold cross-validation. On the only publicly available pelvic fracture X-ray dataset (PXR150), PelviNeXt exceeds previously reported state-of-the-art results across accuracy, recall, specificity, and AUROC. Ablation studies confirm that each architectural component contributes to performance on both tasks. Our results demonstrate that a single architecture, applied without task-specific modification, can serve as a reliable foundation for pelvic imaging across modalities in data-scarce, under-researched areas of women's health.
CommentsAccepted at MICCAI CAPI-WOMEN 2026
