Výsledky vyhledávání - "Valentinitsch A"

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Geometrically Inspired Kernel Machines for Collaborative Learning Beyond Gradient Descent

Autor: Kumar, Mohit, Valentinitsch, Alexander, Fuchs, Magdalena, Brucker, Mathias, Bowles, Juliana, Husakovic, Adnan, Abbas, Ali, Moser, Bernhard A.

This paper develops a novel mathematical framework for collaborative learning by means of geometrically inspired kernel machines which includes statements on the bounds of generalisation and approximation errors, and sample complexity. For classifica

Externí odkaz: http://arxiv.org/abs/2407.04335

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VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images

Autor: Sekuboyina, Anjany, Husseini, Malek E., Bayat, Amirhossein, Löffler, Maximilian, Liebl, Hans, Li, Hongwei, Tetteh, Giles, Kukačka, Jan, Payer, Christian, Štern, Darko, Urschler, Martin, Chen, Maodong, Cheng, Dalong, Lessmann, Nikolas, Hu, Yujin, Wang, Tianfu, Yang, Dong, Xu, Daguang, Ambellan, Felix, Amiranashvili, Tamaz, Ehlke, Moritz, Lamecker, Hans, Lehnert, Sebastian, Lirio, Marilia, de Olaguer, Nicolás Pérez, Ramm, Heiko, Sahu, Manish, Tack, Alexander, Zachow, Stefan, Jiang, Tao, Ma, Xinjun, Angerman, Christoph, Wang, Xin, Brown, Kevin, Kirszenberg, Alexandre, Puybareau, Élodie, Chen, Di, Bai, Yiwei, Rapazzo, Brandon H., Yeah, Timyoas, Zhang, Amber, Xu, Shangliang, Hou, Feng, He, Zhiqiang, Zeng, Chan, Xiangshang, Zheng, Liming, Xu, Netherton, Tucker J., Mumme, Raymond P., Court, Laurence E., Huang, Zixun, He, Chenhang, Wang, Li-Wen, Ling, Sai Ho, Huynh, Lê Duy, Boutry, Nicolas, Jakubicek, Roman, Chmelik, Jiri, Mulay, Supriti, Sivaprakasam, Mohanasankar, Paetzold, Johannes C., Shit, Suprosanna, Ezhov, Ivan, Wiestler, Benedikt, Glocker, Ben, Valentinitsch, Alexander, Rempfler, Markus, Menze, Björn H., Kirschke, Jan S.

Publikováno v: Medical Image Analysis, Volume 73, October 2021, 102166

Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision-support systems for diagnosis, surgery planning, and p

Externí odkaz: http://arxiv.org/abs/2001.09193

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Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysis

Autor: Sekuboyina, Anjany, Rempfler, Markus, Valentinitsch, Alexander, Loeffler, Maximilian, Kirschke, Jan S., Menze, Bjoern H.

We propose an auto-encoding network architecture for point clouds (PC) capable of extracting shape signatures without supervision. Building on this, we (i) design a loss function capable of modelling data variance on PCs which are unstructured, and (

Externí odkaz: http://arxiv.org/abs/1907.09254

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Labelling Vertebrae with 2D Reformations of Multidetector CT Images: An Adversarial Approach for Incorporating Prior Knowledge of Spine Anatomy

Autor: Sekuboyina, Anjany, Rempfler, Markus, Valentinitsch, Alexander, Menze, Bjoern H., Kirschke, Jan S.

Purpose: To use and test a labelling algorithm that operates on two-dimensional (2D) reformations, rather than three-dimensional (3D) data to locate and identify vertebrae. Methods: We improved the Btrfly Net (described by Sekuboyina et al) that work

Externí odkaz: http://arxiv.org/abs/1902.02205

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Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior

Autor: Sekuboyina, Anjany, Rempfler, Markus, Kukačka, Jan, Tetteh, Giles, Valentinitsch, Alexander, Kirschke, Jan S., Menze, Bjoern H.

Robust localisation and identification of vertebrae is essential for automated spine analysis. The contribution of this work to the task is two-fold: (1) Inspired by the human expert, we hypothesise that a sagittal and coronal reformation of the spin

Externí odkaz: http://arxiv.org/abs/1804.01307

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A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets

Autor: Sekuboyina, Anjany, Valentinitsch, Alexander, Kirschke, Jan S., Menze, Bjoern H.

Multi-class segmentation of vertebrae is a non-trivial task mainly due to the high correlation in the appearance of adjacent vertebrae. Hence, such a task calls for the consideration of both global and local context. Based on this motivation, we prop

Externí odkaz: http://arxiv.org/abs/1703.04347

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SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks

Autor: Christ, Patrick Ferdinand, Ettlinger, Florian, Kaissis, Georgios, Schlecht, Sebastian, Ahmaddy, Freba, Grün, Felix, Valentinitsch, Alexander, Ahmadi, Seyed-Ahmad, Braren, Rickmer, Menze, Bjoern

Automatic non-invasive assessment of hepatocellular carcinoma (HCC) malignancy has the potential to substantially enhance tumor treatment strategies for HCC patients. In this work we present a novel framework to automatically characterize the maligna

Externí odkaz: http://arxiv.org/abs/1702.05941

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