Advanced Computational Methods for Oncological Image Analysis

• Kolejni autorzy: Rundo, Leonardo (Redaktor), Militello, Carmelo (Redaktor), Conti, Vincenzo (Redaktor), Zaccagna, Fulvio (Redaktor), Han, Changhee (Redaktor)
• Format: Elektroniczne Rozdział
• Język: angielski
• Wydane: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
• Hasła przedmiotowe: Medicine; 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; magnetic resonance imaging; survey; brain MRI image; tumor region; skull stripping; region growing; U-Net; BRATS dataset; 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; texture; mammography; medical imaging; imaging biomarkers; bone scintigraphy; prostate cancer; semisupervised classification; false positives reduction; computer-aided detection; breast mass; mass detection; mass segmentation; Mask R-CNN; dataset partition; brain tumor; classification; shallow machine learning; breast cancer diagnosis; Wisconsin Breast Cancer Dataset; feature selection; dimensionality reduction; principal component analysis; ensemble method; n/a
• Dostęp online: DOAB: download the publication; DOAB: description of the publication

[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. T...