← Back to all news
arXiv Computer Vision By Xinge Guo, Yuanhao Wang, Liqi Shu, Yang Liu, Min Xu

ConPro: Contrast Projection Pretraining for Label-Efficient Vessel Segmentation in DSA Sequences

Read the original on arXiv Computer Vision →

ConPro introduces a self‑supervised pretraining method for vessel segmentation in digital subtraction angiography (DSA) by using a contrast projection target— the normalized drop of each pixel below its temporal median. On the DIAS and DSCA datasets, ConPro outperforms training from scratch across 10%, 20%, and 50% labeled data, and it is the best among compared methods on DSCA at 20% and 50% labels. When combined with semi‑supervised training, ConPro‑derived weights boost the UniMatch baseline by 0.5–2.0 Dice points and 0.9–2.3 clDice points, achieving 75.4 Dice on DIAS and 81.3 on DSCA.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

Hugging Face Trending Papers
Aug 19

X-LMC: Cross-View Spatiotemporal Collateral Circulation Scoring from DSA

X‑LMC is a spatiotemporal deep‑learning framework that automatically scores leptomeningeal collateral (LMC) status from time‑resolved biplane digital subtraction angiography (DSA). It uses a DINOv2 backbone to encode spatial frames, a token‑level cross‑view attention module to fuse orthogonal projections, and a recurrent network to model contrast bolus dynamics. On a multicenter dataset of 134 M1‑segment occlusion patients, X‑LMC achieved a Quadratic Weighted Kappa of 0.398 and a macro‑F1 of 0.711, outperforming static and other spatiotemporal baselines and matching clinical inter‑rater agreement.

benchmarks
More like this →