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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Xehetasun bibliografikoak
Formatua:	Online
Hizkuntza:	ingelesa
Argitaratua:	MDPI - Multidisciplinary Digital Publishing Institute 2022
Gaiak:	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 thema EDItEUR::M Medicine and Nursing
Sarrera elektronikoa:	ONIX_20220111_9783036525549_875
Etiketak:	Etiketa erantsi Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!

Advanced Computational Methods for Oncological Image Analysis

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