TMI202106论文汇总(IEEETransactionsonMedicalImaging)

TMI202106论⽂汇总（IEEETransactionsonMedicalImaging）

1. Segmentation-Renormalized Deep Feature Modulation for Unpaired Image Harmonization

⽤于不成对图像协调的分割重归⼀化深度特征调制

Mengwei Ren. Neel Dey. James Fishbaugh. Guido Gerig.

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Deep networks are now ubiquitous in large-scale multi-center imaging studies. However, the direct aggregation of

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Deep learning has successfully been leveraged for medical image gmentation. It employs convolutional neural

networks (CNN) to learn distinctive image features from a defined pixel-wi objective function. However, this

approach can lead to less output pixel interdependence producing incomplete and unrealistic gmentation results. In

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Diagnostic lung imaging is often associated with high radiation do and lacks nsitivity, especially for diagnosing

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Recently, automatic diagnostic approaches have been widely ud to classify ocular dias. Most of the

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The fusion of multi-modal data (e.g., magnetic resonance imaging (MRI) and positron emission tomography (PET)) has

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Lag signals occur at images quentially acquired from a flat-panel (FP) dynamic detector in fluoroscopic imaging due

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Is it possible to find deterministic relationships between optical measurements and pathophysiology in an unsupervid

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Our approach differs from the usual global measure of cardiac efficiency by using PET/MRI to measure efficiency of