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Deep learning aids radiomic feature selection for lung cancer detection

By PulseAugur Editorial · [3 sources] ·

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.

RANK_REASON The cluster contains a research paper detailing a new methodology for feature selection in medical imaging analysis.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

Deep learning aids radiomic feature selection for lung cancer detection

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The cluster contains a research paper detailing a new methodology for feature selection in medical imaging analysis.
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COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Hina Shakir, Mohammad Mohatram, Javeed Hussain, Syed Rizwan Ali, Muhammad Irfan Memon ·

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

    arXiv:2606.04453v1 Announce Type: cross Abstract: Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis. However, radiomics datasets are typically high-dimensional with limited samp…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

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

    Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis. However, radiomics datasets are typically high-dimensional with limited samples, making feature selection a critical step for …

  3. arXiv cs.CV TIER_1 English(EN) · Muhammad Irfan Memon ·

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

    Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis. However, radiomics datasets are typically high-dimensional with limited samples, making feature selection a critical step for …

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