步履蹒跚
发表于 2025-3-25 04:23:34
Cross-Modality Image Synthesis from Unpaired Data Using CycleGAN,oft tissue contrast. However, MRI has poor contrast for bone structures. Clearly, it would be helpful if a corresponding CT were available, as bone boundaries are more clearly seen and CT has a standardized (i.e., Hounsfield) unit. Therefore, we aim at MR-to-CT synthesis. While the CycleGAN was succ
Ergots
发表于 2025-3-25 10:40:30
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误传
发表于 2025-3-25 11:51:10
Unsupervised Learning for Cross-Domain Medical Image Synthesis Using Deformation Invariant Cycle Con-specific nonlinear deformations captured by CycleGAN make the synthesized images difficult to be used for some applications, for example, generating pseudo-CT for PET-MR attenuation correction. This paper presents a deformation-invariant CycleGAN (DicycleGAN) method using deformable convolutional
maladorit
发表于 2025-3-25 18:38:52
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lactic
发表于 2025-3-25 23:17:10
Model-Based Generation of Synthetic 3D Time-Lapse Sequences of Multiple Mutually Interacting Motileich hinders objective benchmarking of bioimage analysis workflows as well as training of widespread deep-learning-based approaches. In this paper, we present a novel simulation system capable of generating synthetic 3D time-lapse sequences of multiple mutually interacting cells with filopodial protr
啪心儿跳动
发表于 2025-3-26 01:09:43
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闪光东本
发表于 2025-3-26 04:34:34
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榨取
发表于 2025-3-26 10:47:58
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隐藏
发表于 2025-3-26 15:24:03
Lung Nodule Synthesis Using CNN-Based Latent Data Representation,large amounts of annotated training data. When it comes to Medical Imaging, annotation is often complicated and/or expensive, and innovative methods for dealing with small or very imbalanced training sets are mostly welcome. In this context, this paper proposes a novel approach for efficiently synth
Callus
发表于 2025-3-26 20:18:39
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