TY - BOOK ID - 146243117 TI - Advanced Computational Methods for Oncological Image Analysis AU - Rundo, Leonardo AU - Militello, Carmelo AU - Conti, Vincenzo AU - Zaccagna, Fulvio AU - Han, Changhee PY - 2021 PB - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - Medicine KW - melanoma detection KW - deep learning KW - transfer learning KW - ensemble classification KW - 3D-CNN KW - immunotherapy KW - radiomics KW - self-attention KW - breast imaging KW - microwave imaging KW - image reconstruction KW - segmentation KW - unsupervised machine learning KW - k-means clustering KW - Kolmogorov-Smirnov hypothesis test KW - statistical inference KW - performance metrics KW - contrast source inversion KW - brain tumor segmentation KW - magnetic resonance imaging KW - survey KW - brain MRI image KW - tumor region KW - skull stripping KW - region growing KW - U-Net KW - BRATS dataset KW - incoherent imaging KW - clutter rejection KW - breast cancer detection KW - MRgFUS KW - proton resonance frequency shift KW - temperature variations KW - referenceless thermometry KW - RBF neural networks KW - interferometric optical fibers KW - breast cancer KW - risk assessment KW - machine learning KW - texture KW - mammography KW - medical imaging KW - imaging biomarkers KW - bone scintigraphy KW - prostate cancer KW - semisupervised classification KW - false positives reduction KW - computer-aided detection KW - breast mass KW - mass detection KW - mass segmentation KW - Mask R-CNN KW - dataset partition KW - brain tumor KW - classification KW - shallow machine learning KW - breast cancer diagnosis KW - Wisconsin Breast Cancer Dataset KW - feature selection KW - dimensionality reduction KW - principal component analysis KW - ensemble method KW - melanoma detection KW - deep learning KW - transfer learning KW - ensemble classification KW - 3D-CNN KW - immunotherapy KW - radiomics KW - self-attention KW - breast imaging KW - microwave imaging KW - image reconstruction KW - segmentation KW - unsupervised machine learning KW - k-means clustering KW - Kolmogorov-Smirnov hypothesis test KW - statistical inference KW - performance metrics KW - contrast source inversion KW - brain tumor segmentation KW - magnetic resonance imaging KW - survey KW - brain MRI image KW - tumor region KW - skull stripping KW - region growing KW - U-Net KW - BRATS dataset KW - incoherent imaging KW - clutter rejection KW - breast cancer detection KW - MRgFUS KW - proton resonance frequency shift KW - temperature variations KW - referenceless thermometry KW - RBF neural networks KW - interferometric optical fibers KW - breast cancer KW - risk assessment KW - machine learning KW - texture KW - mammography KW - medical imaging KW - imaging biomarkers KW - bone scintigraphy KW - prostate cancer KW - semisupervised classification KW - false positives reduction KW - computer-aided detection KW - breast mass KW - mass detection KW - mass segmentation KW - Mask R-CNN KW - dataset partition KW - brain tumor KW - classification KW - shallow machine learning KW - breast cancer diagnosis KW - Wisconsin Breast Cancer Dataset KW - feature selection KW - dimensionality reduction KW - principal component analysis KW - ensemble method UR - https://www.unicat.be/uniCat?func=search&query=sysid:146243117 AB - [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.] ER -