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Advanced Computational Methods for Oncological Image Analysis
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[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.]
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Keywords
- 3D-CNN
- bone scintigraphy
- brain MRI image
- brain tumor
- brain tumor segmentation
- BRATS dataset
- Breast cancer
- breast cancer detection
- breast cancer diagnosis
- breast imaging
- breast mass
- Classification
- clutter rejection
- computer-aided detection
- contrast source inversion
- dataset partition
- deep learning
- Dimensionality Reduction
- ensemble classification
- ensemble method
- false positives reduction
- feature selection
- Image reconstruction
- Imaging biomarkers
- Immunotherapy
- incoherent imaging
- interferometric optical fibers
- K-means clustering
- Kolmogorov-Smirnov hypothesis test
- Machine learning
- Magnetic Resonance Imaging
- mammography
- mask R-CNN
- mass detection
- mass segmentation
- Medical imaging
- medicine
- melanoma detection
- microwave imaging
- MRgFUS
- n/a
- performance metrics
- principal component analysis
- prostate cancer
- proton resonance frequency shift
- radiomics
- RBF neural networks
- referenceless thermometry
- region growing
- Risk assessment
- segmentation
- self-attention
- semisupervised classification
- shallow machine learning
- skull stripping
- statistical inference
- survey
- temperature variations
- Texture
- transfer learning
- tumor region
- u-net
- Unsupervised machine learning
- Wisconsin Breast Cancer Dataset
Links
DOI: 10.3390/books978-3-0365-2555-6Editions