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Advanced Computational Methods for Oncological Image Analysis

[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...

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Bibliographic Details
Main Author: Rundo, Leonardo
Other Authors: Militello, Carmelo, Conti, Vincenzo, Zaccagna, Fulvio
Format: eBook
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
Subjects:
Machine Learning
Mrgfus
Rbf Neural Networks
Bone Scintigraphy
Radiomics
Contrast Source Inversion
Breast Mass
Performance Metrics
Brain Mri Image
Brain Tumor
Kolmogorov-smirnov Hypothesis Test
Survey
U-net
Skull Stripping
K-means Clustering
N/a
Feature Selection
Wisconsin Breast Cancer Dataset
Deep Learning
Referenceless Thermometry
Incoherent Imaging
Interferometric Optical Fibers
Proton Resonance Frequency Shift
Mass Segmentation
Clutter Rejection
Immunotherapy
Ensemble Classification
Transfer Learning
Principal Component Analysis
Risk Assessment
Semisupervised Classification
Microwave Imaging
Prostate Cancer
Breast Cancer
Magnetic Resonance Imaging
Mammography
Medical Imaging
Statistical Inference
Tumor Region
Dimensionality Reduction
Unsupervised Machine Learning
Breast Imaging
Breast Cancer Detection
Imaging Biomarkers
Self-attention
Segmentation
Image Reconstruction
Melanoma Detection
Mass Detection
Ensemble Method
Breast Cancer Diagnosis
Temperature Variations
Computer-aided Detection
Brain Tumor Segmentation
3d-cnn
Dataset Partition
Classification
Mask R-cnn
Texture
Shallow Machine Learning
False Positives Reduction
Brats Dataset
Medicine And Nursing / Bicssc
Region Growing
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Description
Summary:[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.]
Item Description:Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
Physical Description:1 electronic resource (262 p.)
ISBN:books978-3-0365-2555-6
9783036525549
9783036525556

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