ABHOR
发表于 2025-3-28 15:25:47
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
飞镖
发表于 2025-3-28 18:59:02
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
来就得意
发表于 2025-3-28 23:10:51
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gastritis
发表于 2025-3-29 06:03:09
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拒绝
发表于 2025-3-29 09:28:13
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
陈腐思想
发表于 2025-3-29 11:33:41
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
极大的痛苦
发表于 2025-3-29 18:02:59
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起皱纹
发表于 2025-3-29 21:27:15
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hazard
发表于 2025-3-30 03:27:20
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
发表于 2025-3-30 06:39:03
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