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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. RESEARCH · arXiv cs.CV English(EN) · · [2 sources]

    An Uncertainty Estimation Framework for Dose Accumulation in Adaptive Radiotherapy: Application to CBCT-Guided Radiotherapy for Cervical Cancer

    Researchers have developed IMPACT-DoseAcc, a new framework for estimating cumulative radiation dose in adaptive radiotherapy (ART) for cervical cancer. This system quantifies uncertainties arising from image registration and segmentation, providing probabilistic dose-volume histograms. The framework demonstrated strong correlation between registration uncertainty and geometric error, achieving high coverage for target volumes and stabilizing dose estimates. AI

    IMPACT Improves interpretation of cumulative dose in ART, supporting more reproducible and uncertainty-informed treatment workflows.

  2. RESEARCH · arXiv cs.CV English(EN) · · [3 sources]

    Radiomic Feature Selection Using Gradient Loss of Deep Neural Network for Lung Cancer Stage Detection

    Researchers have developed a new framework called Gradient-Loss Recursive Feature Elimination (GL-RFE) to improve the selection of radiomic features for lung cancer stage detection. This method uses a deep neural network's gradient sensitivity analysis to identify the most impactful features from high-dimensional medical imaging data. The GL-RFE framework successfully identified a top set of 15 features, which were then used to train a classifier achieving over 90% accuracy in distinguishing between early and advanced lung cancer stages. AI

    IMPACT Enhances AI's role in medical diagnostics by improving feature selection for high-dimensional imaging data.