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Precision-medicine-toolbox: An open-source python package for facilitation of quantitative medical imaging and radiomics analysis

arXiv 2202.13965 ↗First posted 28 Feb 2022eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

Medical image analysis plays a key role in precision medicine as it allows the clinicians to identify anatomical abnormalities and it is routinely used in clinical assessment. Data curation and pre-processing of medical images are critical steps in the quantitative medical image analysis that can have a significant impact on the resulting model performance. In this paper, we introduce a precision-medicine-toolbox that allows researchers to perform data curation, image pre-processing and handcrafted radiomics extraction (via Pyradiomics) and feature exploration tasks with Python. With this open-source solution, we aim to address the data preparation and exploration problem, bridge the gap between the currently existing packages, and improve the reproducibility of quantitative medical imaging research.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 63 references that are in the map. First posted on arXiv 28 Feb 2022.

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References · 7

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
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  3. 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 660
  4. 2020A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promisescited by 230
  5. 2016Image biomarker standardisation initiativecited by 61
  6. 2018Deep learning in radiology: an overview of the concepts and a survey of the state of the artcited by 34
  7. 2020pymia: A Python package for data handling and evaluation in deep learning-based medical image analysiscited by 16

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