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0 (hodnocen0 x )

EB

ONLINE

1st ed.

Linkoping : Linkopings Universitet, 2021

1 online resource (101 pages)

ISBN 9789179290382 (electronic bk.)

Print version: Cirillo, Marco Domenico A Path along Deep Learning for Medical Image Analysis Linkoping : Linkopings Universitet,c2021

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.

001896334

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(Au-PeEL)EBL6790370

(MiAaPQ)EBC6790370

(OCoLC)1283844869