TY  - BOOK
AU  - Cirillo,Marco Domenico
TI  - A Path along Deep Learning for Medical Image Analysis: With Focus on Burn Wounds and Brain Tumors
T2  - Link�oping Studies in Science and Technology. Dissertations Series
SN  - 9789179290382
PY  - 2021///
CY  - Link�oping
PB  - Linkopings Universitet
KW  - Electronic books
N1  - Intro -- Abstract -- Acknowledgments -- Contents -- List of Figures -- Introduction -- Aim -- Delimitations -- Research questions -- Included papers -- Research ethics -- Outline -- Burn Wounds and Brain Tumors -- Burn wounds -- Pathophysiology -- Assessment methods -- Brain tumors -- Pathophysiology -- Assessment methods -- Reflections -- Image Features -- Type of features -- Color features -- Edge feature -- Texture features -- Mixed features -- Principal component analysis -- Independent component analysis -- Tensor decomposition -- Deep features -- Convolution -- Deep features -- Reflections -- Convolutional Neural Networks -- Deep learning basics -- Loss functions -- Forward and backward propagation -- Data pre-processing -- Weight initialization -- Normalization layers -- Activation functions -- Optimization -- Regularization -- Residual block -- Convolutional neural networks -- Convolutional layers -- CNNs for image classification -- CNNs for image segmentation -- CNNs for image generation -- Reflections -- Image Augmentation -- Image Augmentation Techniques -- Patch extraction -- Flipping -- Rotation -- Scaling -- Elastic grid-based deformation -- Brightness -- Reflections -- Generative Adversarial Networks -- Generator and discriminator -- GANs in medical imaging -- GAN losses -- Image-to-image GANs -- Pix2Pix -- Semantic image synthesis with spatially-adaptive normalization -- Reflections -- Papers, Discussions and Conclusions -- Paper I: Tensor decomposition for colour image segmentation of burn wounds -- Paper II: Time-independent prediction of burn depth using deep convolutional neural networks -- Paper III: Improving burn depth assessment for pediatric scalds by AI based on semantic segmentation of polarized light photography images -- Paper IV: Vox2Vox: 3D-GAN for brain tumour segmentation; Paper V: What is the best data augmentation for 3D brain tumor segmentation? -- Conclusions -- Bibliography -- Papers
UR  - https://ebookcentral.proquest.com/lib/bacm-ebooks/detail.action?docID=6790370
ER  -