ArticleDiagnostics (Basel, Switzerland)2023
Deep Features from Pretrained Networks Do Not Outperform Hand-Crafted Features in Radiomics.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- AI-MIRACLE: Artificial Intelligence and MultIpaRAmetric MRI Predict CLinical OutcomEs to Neoadjuvant Immunotherapy in Patients with Muscle-invasive Bladder Cancer Undergoing Radical Cystectomy.European urology oncology · 2026Article
- An interpretable PET/CT-based radiomic-clinical model for predicting bone marrow involvement in follicular lymphoma: comparison of pelvic and spine-pelvis VOI frameworks.European journal of nuclear medicine and molecular imaging · 2026Article
- Can 3D T1 Post-Contrast MRI in A Radiomics-Machine Learning Model Distinguish Infective from Neoplastic Ring-Enhancing Brain Lesions? An Exploratory Study.Diagnostics (Basel, Switzerland) · 2026Article
- Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: CT vs MRI.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- A multicenter deep learning framework integrating radiomics and vision transformers for comprehensive ovarian tumor analysis from ultrasound imaging.European journal of medical research · 2026Article
- Multimodal AI in high-grade serous ovarian cancer: integrated prediction and clinical decision-making.Frontiers in oncology · 2026Review
- Automated detection of retinal artery occlusion in fundus photography via self-supervised deep learning and multimodal interpretability using a multimodal AI chatbot.Medical & biological engineering & computing · 2025Article
- Reproducibility and interpretability in radiomics: a critical assessmentDiagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- Overlooked and underpowered: a meta-research addressing sample size in radiomics prediction models for binary outcomes.European radiology · 2025Article
- Decoding Radiomics: A Step-by-Step Guide to Machine Learning Workflow in Hand-Crafted and Deep Learning Radiomics Studies.Diagnostics (Basel, Switzerland) · 2024Review
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Authors and funding
1 author.
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Abstract
In radiomics, utilizing features extracted from pretrained deep networks could result in models with a higher predictive performance than those relying on hand-crafted features. This study compared the predictive performance of models trained with either deep features, hand-crafted features, or a combination of these features in terms of the area under the receiver-operating characteristic curve (AUC) and other metrics. We trained models on ten radiological datasets using five feature selection methods and three classifiers. Our results indicate that models based on deep features did not show an improved AUC compared to those utilizing hand-crafted features (deep: AUC 0.775, hand-crafted: AUC 0.789;
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