Výsledky vyhledávání - "Husseini, Malek"

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Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes

Autor: Sinhamahapatra, Poulami, Shit, Suprosanna, Sekuboyina, Anjany, Husseini, Malek, Schinz, David, Lenhart, Nicolas, Menze, Joern, Kirschke, Jan, Roscher, Karsten, Guennemann, Stephan

Publikováno v: Machine.Learning.for.Biomedical.Imaging. 2 (2024)

Vertebral fracture grading classifies the severity of vertebral fractures, which is a challenging task in medical imaging and has recently attracted Deep Learning (DL) models. Only a few works attempted to make such models human-interpretable despite

Externí odkaz: http://arxiv.org/abs/2404.02830

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A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data

Autor: Liebl, Hans, Schinz, David, Sekuboyina, Anjany, Malagutti, Luca, Löffler, Maximilian T., Bayat, Amirhossein, Husseini, Malek El, Tetteh, Giles, Grau, Katharina, Niederreiter, Eva, Baum, Thomas, Wiestler, Benedikt, Menze, Bjoern, Braren, Rickmer, Zimmer, Claus, Kirschke, Jan S.

With the advent of deep learning algorithms, fully automated radiological image analysis is within reach. In spine imaging, several atlas- and shape-based as well as deep learning segmentation algorithms have been proposed, allowing for subsequent au

Externí odkaz: http://arxiv.org/abs/2103.06360

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Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection

Autor: Husseini, Malek, Sekuboyina, Anjany, Loeffler, Maximilian, Navarro, Fernando, Menze, Bjoern H., Kirschke, Jan S.

Osteoporotic vertebral fractures have a severe impact on patients' overall well-being but are severely under-diagnosed. These fractures present themselves at various levels of severity measured using the Genant's grading scale. Insufficient annotated

Externí odkaz: http://arxiv.org/abs/2008.07831

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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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