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.
arXiv:2607. 24453v1 Announce Type: cross Abstract: Learning from minimal human supervision is a long-standing goal in medical image analysis, where dense expert annotations are costly.
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.
Self-supervised learning (SSL) can reduce the need for labelled medical images, but the choice of pretext objective remains unclear for lung ultrasound (LUS). Contrastive learning, masked reconstructi...
arXiv:2609.12834v1 Announce Type: new Abstract: Modelling how a disease progresses over time requires longitudinal imaging cohorts, which are scarce and small, whereas cross-sectional data -- one ima...
arXiv:2609.25850v1 Announce Type: new Abstract: Deep learning performance generally improves with increasing training data, yet this scaling is fundamentally constrained by annotation cost in large-s...
arXiv:2610.01542v1 Announce Type: new Abstract: Cerebral microbleeds (CMBs) and cortical superficial siderosis (cSS) are imaging markers of cerebral small vessel disease, but their automated segmenta...
DART is a new RGB‑D pretraining method for surgical vision foundation models that incorporates pseudo‑labeled depth maps as a pixel‑space reconstruction target during training. By adding a depth reconstruction head to DINOv2’s masked iBOT framework, DART improves representation quality without affecting downstream RGB‑only fine‑tuning or inference. Across eight surgical benchmarks—including segmentation, depth estimation, and image‑level recognition—DART outperforms both natural‑image and in‑domain baselines, demonstrating that geometric pseudo‑labels can strengthen foundation model pretraining without extra labels or inference cost.
arXiv:2609.16551v1 Announce Type: new Abstract: Self-supervised learning (SSL) can reduce the need for labelled medical images, but the choice of pretext objective remains unclear for lung ultrasound...
arXiv:2607. 22139v1 Announce Type: cross Abstract: Accurate pixel-level classification of coronary angiograms is critical for cardiovascular disease assessment, yet the field lacks standardized evaluation protocols.
arXiv:2609.21412v1 Announce Type: new Abstract: Medical image segmenters often get worse when sites, scanner vendors, or protocols change. Continual test-time adaptation (CTTA) addresses this problem...
arXiv:2608.31073v1 Announce Type: new Abstract: Whole-heart segmentation (WHS) in computed tomography (CT) and magnetic resonance imaging (MRI) is affected by acquisition shifts and heterogeneous car...
arXiv:2605. 23995v4 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) is increasingly used in medical image analysis to reduce dependence on costly expert annotations by learning transferable representations from unlabeled data.