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

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MetaViT: Metabolism-Aware Vision Transformer for?Differential Diagnosis of?Parkinsonism with?,F-FDG ) is crucial for informing prognosis and determining treatment strategies. Current automated differential diagnosis methods for .F-fluorodeoxyglucose (.F-FDG) positron emission tomography (PET) scans, such as convolutional neural networks (CNNs), often focus on local brain regions and do not explici

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Multi-task Multi-instance Learning for?Jointly Diagnosis and?Prognosis of?Early-Stage Breast Invasivd to estimate the clinical outcome of human cancers. However, most of the existing studies treat the prognosis and diagnosis tasks separately, which overlooks the fact that the diagnosis information indicating the severity of the disease that is highly related to the patients’ survival. In addition,

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On Fairness of?Medical Image Classification with?Multiple Sensitive Attributes via?Learning Orthogontreatments for patients with multiple sensitive demographic attributes, which is a crucial yet challenging problem for real-world clinical applications. In this paper, we propose a novel method for fair representation learning with respect to multi-sensitive attributes. We pursue the independence be

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Pixel-Level Explanation of?Multiple Instance Learning Models in?Biomedical Single Cell Imagesg provides instance-level explainability, however for many clinical applications a deeper, pixel-level explanation is desirable, but missing so far. In this work, we investigate the use of four attribution methods to explain a multiple instance learning models: GradCAM, Layer-Wise Relevance Propagat

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Transient Hemodynamics Prediction Using an?Efficient Octree-Based Deep Learning Modellities are not able to accurately acquire high-resolution hemodynamic information that would be required to assess complex neurovascular pathologies. Instead, computational fluid dynamics (CFD) simulations can be applied to tomographic reconstructions to obtain clinically relevant information. Howev

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Weakly Semi-supervised Detection in?Lung Ultrasound Videosata. We propose a method for improving object detection in medical videos through weak supervision from video-level labels. More concretely, we aggregate individual detection predictions into video-level predictions and extend a teacher-student training strategy to provide additional supervision via

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