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

CRUC

ENACT

Abstracting Volumetric Medical Images with Sparse Keypoints for Efficient Geometric Segmentation oft for detecting thin structures that make up a tiny fraction of the entire image volume. We propose a geometric deep learning framework that leverages the representation of the image as a keypoint (KP) cloud and segments it with a graph convolutional network (GCN). From the sparse point segmentation

charisma

1431-472X Industrie und Anwendern.Seit mehr als 25 Jahren ist der Workshop "Bildverarbeitung für die Medizin" als erfolgreiche Veranstaltung etabliert. Ziel ist auch 2024 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespräche zwischen Wissenschaftlern, Industrie und Anwendern.

senile-dementia

罗盘

Keynote: Recent Advances in Surgical AI for Next Generation Interventions,s, ultimately reducing cognitive load on surgeons and optimizing procedural efficiency. This talk will highlight AI applications in different surgical procedures and where we stand in terms of their clinical translation for moving towards next generation of surgical intervention. [1].

良心

传授知识

Islamophobia and Radicalizationn imaging platform (https://stroke.neuroAI-HD.org) for online processing of medical imaging data with the developed ANN, including provisions for data crowdsourcing. Notably, this work has previously been published in Nature Communications [1].

供过于求

物种起源

https://doi.org/10.1007/978-3-658-42193-9 through the sparsity of the point cloud representation while maintaining accuracy. We measure a 34× speed-up at 1.5× the nnU-Net’s error with Förstner KPs and a 6× speed-up at 1.3× error with pre-segmentation KPs.

Feigned

Abstract: Deep Learning-based Detection of Vessel Occlusions on CT-Angiography in Patients with Susn imaging platform (https://stroke.neuroAI-HD.org) for online processing of medical imaging data with the developed ANN, including provisions for data crowdsourcing. Notably, this work has previously been published in Nature Communications [1].