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.
Siegel, Isabelle Soerjomataram, and Ahmedin Jemal
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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.
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.
citing papers explorer
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RadMaps: A Geospatial Framework for Simultaneously Modelling Capacity and Geographic Constraints on Radiotherapy Access
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.
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Generative Modeling of Complex-Valued Brain MRI Data
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.
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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.
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Time-driven Survival Analysis from FDG-PET/CT in Non-Small Cell Lung Cancer
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.