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 samples, making feature selection a critical step for building reliable predictive models. This study proposes a Gradient-Loss Recursive Feature Elimination (GL-RFE) framework that integrates gradient sensitivity analysis from a deep neural network to identify the most influential radiomic features for lung cancer stage de
The proliferation of medical imaging data and advancements in deep learning make it possible to develop sophisticated AI tools for medical diagnosis at this juncture.
This development signifies a growing trend in AI's capability to enhance diagnostic accuracy and personalize treatment, directly impacting healthcare efficiency and patient outcomes.
The method of selecting crucial radiomic features for cancer diagnosis becomes more robust and automated, potentially leading to more reliable AI-driven medical insights.
- · AI healthcare startups
- · Medical imaging companies
- · Oncology departments
- · Patients with lung cancer
- · Traditional manual feature selection methods
- · Inaccurate diagnostic tools
Improved early detection rates and staging accuracy for lung cancer.
Increased adoption of AI in routine clinical practice for various cancers and other diseases.
Reduced healthcare costs due to earlier and more precise diagnoses, potentially extending human longevity and quality of life.
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Read at arXiv cs.LG