Simulation and Synthesis in Medical Imaging

6th International Workshop, SASHIMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings

AvDavid Svoboda,Ninon Burgos

Häftad, Engelska, 2021

Del 12965 i serien Lecture Notes in Computer Science

610 kr

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572 kr

Beskrivning

This book constitutes the refereed proceedings of the 6th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.*The 14 full papers presented were carefully reviewed and selected from 18 submissions. The contributions span the following broad categories in alignment with the initial call-for-papers: methods based on generative models or adversarial learning for MRI/CT/ microscopy image synthesis, and several applications of image synthesis and simulation for data augmentation, image enhancement, or segmentation.*The workshop was held virtually.

Produktinformation

Utgivningsdatum:2021-09-21
Mått:155 x 235 x 10 mm
Vikt:265 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:154
Förlag:Springer Nature Switzerland AG
ISBN:9783030875916

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Artificiell intelligens inom Data och IT
Biovetenskap inom Naturvetenskap och teknik

Innehållsförteckning

Method-Oriented Papers.- Detail matters: high-frequency content for realistic synthetic brain MRI generation.- Joint Image and Label Self-Super-Resolution.- Super-resolution by Latent Space Exploration: Training with Poorly-aligned Clinical and Micro CT Image Dataset.- A Glimpse into the Future: Disease Progression Simulation for Breast Cancer in Mammograms.- Synth-by-Reg (SbR): Contrastive learning for synthesis-based registration of paired images.- Learning-based Template Synthesis For Groupwise Image Registration.- The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties.- Transfer Learning in Optical Microscopy.- X-ray synthesis based on triangular mesh models using GPU-accelerated ray tracing for multi-modal breast image registration.- Application-Oriented Papers.- Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks.- SequenceGAN: Generating Fundus Fluorescence Angiography Sequences from Structure Fundus Image .- Cerebral Blood Volume Prediction based on Multi-modality Magnetic Resonance Imaging.- Cine-MRI simulation to evaluate tumor tracking.- GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models.

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AvDavid Svoboda,Ninon Burgos

Häftad, Engelska, 2021

Del 12965 i serien Lecture Notes in Computer Science

610 kr

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555 kr

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Beskrivning

Produktinformation

Utgivningsdatum:2021-09-21
Mått:155 x 235 x 10 mm
Vikt:265 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:154
Förlag:Springer Nature Switzerland AG
ISBN:9783030875916

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Artificiell intelligens inom Data och IT
Biovetenskap inom Naturvetenskap och teknik

Innehållsförteckning

Method-Oriented Papers.- Detail matters: high-frequency content for realistic synthetic brain MRI generation.- Joint Image and Label Self-Super-Resolution.- Super-resolution by Latent Space Exploration: Training with Poorly-aligned Clinical and Micro CT Image Dataset.- A Glimpse into the Future: Disease Progression Simulation for Breast Cancer in Mammograms.- Synth-by-Reg (SbR): Contrastive learning for synthesis-based registration of paired images.- Learning-based Template Synthesis For Groupwise Image Registration.- The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties.- Transfer Learning in Optical Microscopy.- X-ray synthesis based on triangular mesh models using GPU-accelerated ray tracing for multi-modal breast image registration.- Application-Oriented Papers.- Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks.- SequenceGAN: Generating Fundus Fluorescence Angiography Sequences from Structure Fundus Image .- Cerebral Blood Volume Prediction based on Multi-modality Magnetic Resonance Imaging.- Cine-MRI simulation to evaluate tumor tracking.- GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models.

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