Výsledky vyhledávání - "Sauter, Alexander"

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TotalSegmentator: robust segmentation of 104 anatomical structures in CT images

Autor: Wasserthal, Jakob, Breit, Hanns-Christian, Meyer, Manfred T., Pradella, Maurice, Hinck, Daniel, Sauter, Alexander W., Heye, Tobias, Boll, Daniel, Cyriac, Joshy, Yang, Shan, Bach, Michael, Segeroth, Martin

Publikováno v: Radiol Artif Intell 2023;5(5):e230024

We present a deep learning segmentation model that can automatically and robustly segment all major anatomical structures in body CT images. In this retrospective study, 1204 CT examinations (from the years 2012, 2016, and 2020) were used to segment

Externí odkaz: http://arxiv.org/abs/2208.05868

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A Deep Variational Approach to Clustering Survival Data

Autor: Manduchi, Laura, Marcinkevičs, Ričards, Massi, Michela C., Weikert, Thomas, Sauter, Alexander, Gotta, Verena, Müller, Timothy, Vasella, Flavio, Neidert, Marian C., Pfister, Marc, Stieltjes, Bram, Vogt, Julia E.

In this work, we study the problem of clustering survival data $-$ a challenging and so far under-explored task. We introduce a novel semi-supervised probabilistic approach to cluster survival data by leveraging recent advances in stochastic gradient

Externí odkaz: http://arxiv.org/abs/2106.05763

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Machine Learning Automatically Detects COVID-19 using Chest CTs in a Large Multicenter Cohort

Autor: Barbosa Jr., Eduardo Jose Mortani, Georgescu, Bogdan, Chaganti, Shikha, Aleman, Gorka Bastarrika, Cabrero, Jordi Broncano, Chabin, Guillaume, Flohr, Thomas, Grenier, Philippe, Grbic, Sasa, Gupta, Nakul, Mellot, François, Nicolaou, Savvas, Re, Thomas, Sanelli, Pina, Sauter, Alexander W., Yoo, Youngjin, Ziebandt, Valentin, Comaniciu, Dorin

Objectives: To investigate machine-learning classifiers and interpretable models using chest CT for detection of COVID-19 and differentiation from other pneumonias, ILD and normal CTs. Methods: Our retrospective multi-institutional study obtained 209

Externí odkaz: http://arxiv.org/abs/2006.04998

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Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT

Publikováno v: Radiology: Artificial Intelligence, Vol. 2, No. 4, 2020

Purpose: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations. Materials and Methods: In this retrospective study

Externí odkaz: http://arxiv.org/abs/2004.01279

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