Advanced Computational Methods for Oncological Image Analysis |
Autore | Rundo Leonardo |
Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
Descrizione fisica | 1 electronic resource (262 p.) |
Soggetto topico | Medicine |
Soggetto non controllato |
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 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910557353503321 |
Rundo Leonardo
![]() |
||
Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
![]() | ||
Lo trovi qui: Univ. Federico II | ||
|
Application of Bioinformatics in Cancers |
Autore | Brenner J. Chad |
Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
Descrizione fisica | 1 electronic resource (418 p.) |
Soggetto non controllato |
cancer treatment
extreme learning independent prognostic power AID/APOBEC HP gene inactivation biomarkers biomarker discovery chemotherapy artificial intelligence epigenetics comorbidity score denoising autoencoders protein single-biomarkers gene signature extraction high-throughput analysis concatenated deep feature feature selection differential gene expression analysis colorectal cancer ovarian cancer multiple-biomarkers gefitinib cancer biomarkers classification cancer biomarker mutation hierarchical clustering analysis HNSCC cell-free DNA network analysis drug resistance hTERT variable selection KRAS mutation single-cell sequencing network target skin cutaneous melanoma telomeres Neoantigen Prediction datasets clinical/environmental factors StAR PD-L1 miRNA circulating tumor DNA (ctDNA) false discovery rate predictive model Computational Immunology brain metastases observed survival interval next generation sequencing brain machine learning cancer prognosis copy number aberration mutable motif steroidogenic enzymes tumor mortality tumor microenvironment somatic mutation transcriptional signatures omics profiles mitochondrial metabolism Bufadienolide-like chemicals cancer-related pathways intratumor heterogeneity estrogen locoregionally advanced RNA feature extraction and interpretation treatment de-escalation activation induced deaminase knockoffs R package copy number variation gene loss biomarkers cancer CRISPR overall survival histopathological imaging self-organizing map Network Analysis oral cancer biostatistics firehose Bioinformatics tool alternative splicing biomarkers diseases genes histopathological imaging features imaging TCGA decision support systems The Cancer Genome Atlas molecular subtypes molecular mechanism omics curative surgery network pharmacology methylation bioinformatics neurological disorders precision medicine cancer modeling miRNAs breast cancer detection functional analysis biomarker signature anti-cancer hormone sensitive cancers deep learning DNA sequence profile pancreatic cancer telomerase Monte Carlo mixture of normal distributions survival analysis tumor infiltrating lymphocytes curation pathophysiology GEO DataSets head and neck cancer gene expression analysis erlotinib meta-analysis traditional Chinese medicine breast cancer TCGA mining breast cancer prognosis microarray DNA interaction health strengthening herb cancer genomic instability |
ISBN | 3-03921-789-5 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910367743403321 |
Brenner J. Chad
![]() |
||
MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
![]() | ||
Lo trovi qui: Univ. Federico II | ||
|
Electromagnetic Technologies for Medical Diagnostics. Fundamental Issues, Clinical Applications and Perspectives |
Autore | Crocco Lorenzo |
Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
Descrizione fisica | 1 electronic resource (240 p.) |
Soggetto non controllato |
glucose-dependent dielectric properties
bioradar dielectric measurements dielectric spectroscopy snare RF sensing microwave breast imaging stress detection microwave resonators dielectric properties equipment-related confounders EMR monitoring phantom tissue-related confounders microwave imaging automated breast diagnosis breast cancer detection dielectric characterization blood glucose levels breast cancer diagnosis numerical calculation breast phantoms tomography image-guided on-site validation microwave tomography microwave non-invasive measurement on-body antennas psychophysiological state monitoring thermal ablation microwave ablation biological tissues breast cancer open-ended coaxial probe brain stroke monitoring machine learning patient study UWB diagnostics reconstruction medical radar unobtrusive monitoring medical imaging microwave spectroscopy UWB breast and head phantoms |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910346687003321 |
Crocco Lorenzo
![]() |
||
MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
![]() | ||
Lo trovi qui: Univ. Federico II | ||
|