MSB is a late-fusion stacking framework for multimodal survival prediction under blockwise missingness that improves C-index over baselines on the PIONeeR lung cancer immunotherapy dataset.
and Soerjomataram, Isabelle and Jemal, Ahmedin , title =
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 8roles
background 1polarities
background 1representative citing papers
RadMaps integrates capacity and geographic constraints into a single H3-grid model of radiotherapy access, yielding global estimates of 70% capacity-only, 91% geography-only, and 60% combined access.
A cVAE plus flow-matching model generates realistic complex-valued brain MRI that preserves phase coherence above 0.997 and yields synthetic data that trains abnormality classifiers to 0.880 AUROC, beating the 0.842 real-data baseline on fastMRI.
DoSReMC improves cross-domain generalization in mammography classification by fine-tuning only batch normalization and fully connected layers of pretrained CNNs while preserving convolutional filters, combined with adversarial training.
Multimodal model fuses radiology-report semantics and host-response lab biomarkers to predict lung cancer survival, reporting C-indices of 0.920 (train) and 0.849 (test) in a retrospective two-center cohort of 574 patients.
ATCS and MTS models report mean time-dependent AUCs of 0.794 and 0.793 versus 0.767 for baseline TCS on held-out test data from 292 NSCLC patients.
A time-aware ResNet-based model on PET/CT images improves overall survival prediction in NSCLC by incorporating temporal data, achieving 4.3% higher AUC than fixed-time baselines.
Zivid 2M+ 60 outperformed Intel RealSense D405, PMD Flexx2, and Stereolabs ZED 2i on all tested specimens and metrics; ZED ranked second on real tissue but last on the phantom.
citing papers explorer
-
DoSReMC: Domain Shift Resilient Mammography Classification using Batch Normalization Adaptation
DoSReMC improves cross-domain generalization in mammography classification by fine-tuning only batch normalization and fully connected layers of pretrained CNNs while preserving convolutional filters, combined with adversarial training.