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Deep learning characterization of brain tumours with diffusion weighted imaging

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Publication:2105512
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DOI10.1016/j.jtbi.2022.111342zbMath1503.92046OpenAlexW4308519457MaRDI QIDQ2105512

Sunit Das, Cameron Meaney, Errol Colak, Mohammad Kohandel

Publication date: 8 December 2022

Published in: Journal of Theoretical Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jtbi.2022.111342

zbMATH Keywords

neural networksmedical imagingcancermachine learningPDEsapplied mathematicsdeep learningmathematical oncologymathematical medicine


Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Biomedical imaging and signal processing (92C55)



Uses Software

  • DOLFIN
  • FEniCS
  • Adam


Cites Work

  • An image-driven parameter estimation problem for a reaction-diffusion glioma growth model with mass effects
  • Mathematical biology. Vol. 1: An introduction.
  • Hidden physics models: machine learning of nonlinear partial differential equations
  • Role of hypoxia-activated prodrugs in combination with radiation therapy: an in silico approach
  • Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
  • A mathematical model for brain tumor response to radiation therapy
  • Image-Driven Biophysical Tumor Growth Model Calibration
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