PelviNeXt: A Modality-Agnostic Hybrid Network for Pelvic Imaging in Women's Health

• URL: http://arxiv.org/abs/2608.20144v1
• Date: Thu, 20 Aug 2026 15:05:13 GMT
• Title: PelviNeXt: A Modality-Agnostic Hybrid Network for Pelvic Imaging in Women's Health
• Abstract summary: PelviNeXt is a modality-agnostic hybrid architecture combining a dense convolutional feature extractor, hierarchical channel-spatial attention, and talking-heads self-attention.<n>On the only publicly available pelvic fracture X-ray dataset (PXR150), PelviNeXt exceeds previously reported results across accuracy, recall, specificity, and AUROC.
• Score: 3.270810188656922
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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