Titlebook: Deep Generative Models, and Data Augmentation, Labelling, and Imperfections; First Workshop, DGM4 Sandy Engelhardt,Ilkay Oksuz,Yuan Xue Con

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書目名稱Deep Generative Models, and Data Augmentation, Labelling, and Imperfections被引頻次




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Ultrasound Variational Style Transfer to Generate Images Beyond the Observed Domainent imaging settings. We address these problems using a novel variational style-transfer neural network that can sample various styles from a computed latent space to generate images from a broader domain than what was observed. We show that using our generative approach for ultrasound data augmenta

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Conditional Generation of Medical Images via Disentangled Adversarial Inferencentations of style and content, and use this information to impose control over conditional generation process. We undergo two novel regularization steps to ensure content-style disentanglement. First, we minimize the shared information between content and style by introducing a novel application of

Nefarious

CT-SGAN: Computed Tomography Synthesis GAN the CT-SGAN model that generates large-scale 3D synthetic CT-scan volumes (.) when trained on a small dataset of chest CT-scans. CT-SGAN offers an attractive solution to two major challenges facing machine learning in medical imaging: a small number of given i.i.d. training data, and the restrictio

Nefarious

placebo-effect

CaCL: Class-Aware Codebook Learning for Weakly Supervised Segmentation on Diffuse Image Patternsons (classification), as biomedical images naturally contain image-wise labels in many scenarios. The current weakly supervised learning algorithms from the computer vision community are largely designed for focal objects (e.g., dogs and cats). However, such algorithms are not optimized for diffuse

vascular

BrainNetGAN: Data Augmentation of Brain Connectivity Using Generative Adversarial Network for Dement remains a challenge to identify the individuals at risk of dementia for precise management. Brain MRI offers a non-invasive biomarker to detect brain aging. Previous evidence shows that the structural brain network generated from the diffusion MRI promises to classify dementia accurately based on d

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