Titlebook: Deep Generative Models; Second MICCAI Worksh Anirban Mukhopadhyay,Ilkay Oksuz,Yixuan Yuan Conference proceedings 2022 The Editor(s) (if app

ABHOR

Cross Attention Transformers for?Multi-modal Unsupervised Whole-Body PET Anomaly Detectionthe transformer via cross-attention, i.e. supplying anatomical reference information from paired CT images to aid the PET anomaly detection task. Using 83 whole-body PET/CT samples containing various cancer types, we show that our anomaly detection method is robust and capable of achieving accurate

飛鏢

Interpreting Latent Spaces of?Generative Models for?Medical Images Using Unsupervised Methodsize. Furthermore, the directions show that the generative models capture 3D structure despite being presented only with 2D data. The results show that unsupervised methods to discover interpretable directions in GANs generalize to VAEs and can be applied to medical images. This opens a wide array of

來就得意

gastritis

拒絕

Flow-Based Visual Quality Enhancer for?Super-Resolution Magnetic Resonance Spectroscopic Imagings clinical applications. Deep learning-based super-resolution methods provided promising results for improving the spatial resolution of MRSI, but the super-resolved images are often blurry compared to the experimentally-acquired high-resolution images. Attempts have been made with the generative ad

陳腐思想

Cross Attention Transformers for?Multi-modal Unsupervised Whole-Body PET Anomaly Detectione, stage and predict the evolution of cancer. Due to this heterogeneity, a general-purpose cancer detection model can be built using unsupervised learning anomaly detection models; these models learn a healthy representation of tissue and detect cancer by predicting deviations from healthy appearanc

極大的痛苦

起皺紋

hazard

Learning Generative Factors of?EEG Data with?Variational Auto-Encodersna of interest. We address this challenge by applying the framework of variational auto-encoders to 1) classify multiple pathologies and 2) recover the neurological mechanisms of those pathologies in a data-driven manner. Our framework learns generative factors of data related to pathologies. We pro

mutineer

An Image Feature Mapping Model for?Continuous Longitudinal Data Completion and?Generation of?Synthetlete, or have inconsistencies between observations. Thus, we propose a generative model that not only produces continuous trajectories of fully synthetic patient images, but also imputes missing data in existing trajectories, by estimating realistic progression over time. Our generative model is tra