A Path along Deep Learning for Medical Image Analysis : With Focus on Burn Wounds and Brain Tumors.

By: Cirillo, Marco DomenicoMaterial type: TextTextSeries: Link�oping Studies in Science and Technology. Dissertations SeriesPublisher: Link�oping : Linkopings Universitet, 2021Copyright date: {copy}2021Edition: 1st edDescription: 1 online resource (101 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9789179290382Genre/Form: Electronic books.Additional physical formats: Print version:: A Path along Deep Learning for Medical Image AnalysisOnline resources: Click to View
Contents:
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.
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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.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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