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radMLBench: A dataset collection for benchmarking in radiomics.

Aydin Demircioğlu
Published in: Computers in biology and medicine (2024)
A large collection of datasets, easily accessible via Python, suitable for benchmarking and evaluating new machine learning techniques and methods was curated. Its utility was exemplified by demonstrating that feature decorrelation prior to feature selection does not, on average, lead to significant performance gains and could be omitted, thereby increasing the robustness and reliability of the radiomics pipeline.
Keyphrases
  • machine learning
  • lymph node metastasis
  • deep learning
  • artificial intelligence
  • contrast enhanced
  • big data
  • rna seq
  • squamous cell carcinoma
  • magnetic resonance imaging
  • computed tomography
  • single cell