Titlebook: Information Processing in Medical Imaging; 28th International C Alejandro Frangi,Marleen de Bruijne,Nassir Navab Conference proceedings 202

Cardiac

Resolving Quantitative MRI Model Degeneracy with?Machine Learning via?Training Data Distribution Desng these unknowns, which has traditionally required model fitting - an often iterative procedure, can now be done with one-shot machine learning (ML) approaches. Such parameter estimation may be complicated by intrinsic qMRI signal model degeneracy: different combinations of tissue properties produc

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: A Harmonic Holography for Self-organized Brain Functionacterize spontaneous functional fluctuations, little attention has been paid to the functional mechanisms of neural interactions. Inspired by the notion of holography, we propose an explainable machine learning approach to establishing a novel underpinning of self-organized cross-frequency coupling

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RENAL

mSPD-NN: A Geometrically Aware Neural Framework for?Biomarker Discovery from?Functional Connectomicstivity data. Despite connectomes inhabiting a matrix manifold, most analytical frameworks ignore the underlying data geometry. This is largely because simple operations, such as mean estimation, do not have easily computable closed-form solutions. We propose a geometrically aware neural framework fo

設(shè)施

Diffusion Model Based Semi-supervised Learning on?Brain Hemorrhage Images for?Efficient Midline Shifemorrhage. Existing computational methods on MLS quantification not only require intensive labeling in millimeter-level measurement but also suffer from poor performance due to their dependence on specific landmarks or simplified anatomical assumptions. In this paper, we propose a novel semi-supervi

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牛的細(xì)微差別

Meta-information-Aware Dual-path Transformer for?Differential Diagnosis of?Multi-type Pancreatic Lesomy of pancreatic lesions, i.e., normal, seven major types of lesions, and “other” lesions, is critical to aid the clinical decision-making of patient management and treatment. However, existing work focus on segmentation and classification for very specific lesion types (PDAC) or groups. Moreover,