Advanced Computational Methods for Oncological Image Analysis

Edited by
December 2021
262 pages
  • ISBN978-3-0365-2554-9 (Hardback)
  • ISBN978-3-0365-2555-6 (PDF)

This book is a reprint of the Special Issue Advanced Computational Methods for Oncological Image Analysis that was published in

Computer Science & Mathematics
Physical Sciences

Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology.

Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
melanoma detection; deep learning; transfer learning; ensemble classification; 3D-CNN; immunotherapy; radiomics; self-attention; breast imaging; microwave imaging; image reconstruction; segmentation; unsupervised machine learning; k-means clustering; Kolmogorov-Smirnov hypothesis test; statistical inference; performance metrics; contrast source inversion; brain tumor segmentation; deep learning; magnetic resonance imaging; survey; brain MRI image; tumor region; skull stripping; region growing; U-Net; BRATS dataset; microwave imaging; incoherent imaging; clutter rejection; breast cancer detection; MRgFUS; proton resonance frequency shift; temperature variations; referenceless thermometry; RBF neural networks; interferometric optical fibers; breast cancer; risk assessment; machine learning; deep learning; texture; mammography; medical imaging; imaging biomarkers; radiomics; deep learning; bone scintigraphy; prostate cancer; machine learning; semisupervised classification; false positives reduction; mammography; computer-aided detection; breast mass; mass detection; mass segmentation; Mask R-CNN; dataset partition; brain tumor; classification; segmentation; region growing; shallow machine learning; deep learning; breast cancer diagnosis; Wisconsin Breast Cancer Dataset; feature selection; dimensionality reduction; principal component analysis; ensemble method; n/a