Titlebook: Bildverarbeitung für die Medizin 2024; Proceedings, German Andreas Maier,Thomas M. Deserno,Thomas Tolxdorff Conference proceedings 2024 De

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Uncovering Islamophobic Victimisation, image-based registration methods is not possible and for both expert supervision is desirable. With Lung250M-4B [1], we present a dataset, that aims to tackle these problems. It consists of 248 curated and pre-processed public multi-centric in- and expiratory lung CT scans from 124 patients with la

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Islamophobia, Victimisation and the Veildings indicate that prompting strategies significantly influence segmentation performance. Combining positive points with either bounding boxes or negative points shows substantial benefits, but little to no benefit when combined simultaneously. We further observe that fine-tuning SAM with a few ann

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Keynote: 4-D+ nanoSCOPE Project,by combining state-of-the-art imaging techniques with innovative precision learning software and a novel Xray microscope. Their method has the potential to revolutionize our understanding of bone structure and improve bone remodelling, by enabling an effective assessment of the effects on bone of ag

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Abstract: Flexible Unfolding of Circular Structures for Rendering Textbook-style Cerebrovascular Ma. It extends the As-Rigid- As-Possible (ARAP) deformation algorithm by a smart initialization of the required 3D readout mesh which is fitted to the CoW. Depending on the resulting degree of distortion, it is also possible to merge neighboring arteries directly into the same view. In cases of high d

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Abstract: Interpretable Medical Image Classification Using Prototype Learning and Privileged Informplied to the LIDC-IDRI dataset [2], Proto-Caps predicts the malignancy of lung nodules and also provides prototypical samples that are similar in regards to the nodules’ spiculation, calcification, and six more visual features. Besides the additional interpretability, the proposed solution shows an

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Abstract: Robust Multi-contrast MRI Denoising using Trainable Bilateral Filters without Noise-free n traditional denoising methods and deep learning, we employ a novel approach that combines a neural network comprised of trainable BF layers. This network is trained using an extended version of Stein’s unbiased risk estimator (SURE) as a self-supervised loss function, which estimates the mean squa

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