1 code implementation • 26 Feb 2024 • Hendrik Möller, Robert Graf, Joachim Schmitt, Benjamin Keinert, Matan Atad, Anjany Sekuboyina, Felix Streckenbach, Hanna Schön, Florian Kofler, Thomas Kroencke, Stefanie Bette, Stefan Willich, Thomas Keil, Thoralf Niendorf, Tobias Pischon, Beate Endemann, Bjoern Menze, Daniel Rueckert, Jan S. Kirschke
Training on auto-generated annotations and evaluating on manually corrected test data from the GNC yielded global dice scores of 0. 900 for vertebrae, 0. 960 for intervertebral discs, and 0. 947 for the spinal canal.
2 code implementations • 3 Jul 2023 • Jianxiang Feng, Matan Atad, Ismael Rodríguez, Maximilian Durner, Stephan Günnemann, Rudolph Triebel
Machine Learning (ML) models in Robotic Assembly Sequence Planning (RASP) need to be introspective on the predicted solutions, i. e. whether they are feasible or not, to circumvent potential efficiency degradation.
no code implementations • 21 Mar 2023 • Matthias Keicher, Matan Atad, David Schinz, Alexandra S. Gersing, Sarah C. Foreman, Sophia S. Goller, Juergen Weissinger, Jon Rischewski, Anna-Sophia Dietrich, Benedikt Wiestler, Jan S. Kirschke, Nassir Navab
We then regress the severity of the fracture as a function of the distance to this hyperplane, calibrating the results to the Genant scale.
2 code implementations • 17 Mar 2023 • Matan Atad, Jianxiang Feng, Ismael Rodríguez, Maximilian Durner, Rudolph Triebel
With GRACE, we are able to extract meaningful information from the graph input and predict assembly sequences in a step-by-step manner.
1 code implementation • 15 Jul 2022 • Matan Atad, Vitalii Dmytrenko, Yitong Li, Xinyue Zhang, Matthias Keicher, Jan Kirschke, Bene Wiestler, Ashkan Khakzar, Nassir Navab
Deep learning models used in medical image analysis are prone to raising reliability concerns due to their black-box nature.