Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes

no code implementations • 3 Apr 2024 • Poulami Sinhamahapatra, Suprosanna Shit, Anjany Sekuboyina, Malek Husseini, David Schinz, Nicolas Lenhart, Joern Menze, Jan Kirschke, Karsten Roscher, Stephan Guennemann

In this work, we propose a novel interpretable-by-design method, ProtoVerse, to find relevant sub-parts of vertebral fractures (prototypes) that reliably explain the model's decision in a human-understandable way.

Medical Diagnosis

Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral Fracture Grading

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.

regression

Interpretable Vertebral Fracture Diagnosis

1 code implementation • 30 Mar 2022 • Paul Engstler, Matthias Keicher, David Schinz, Kristina Mach, Alexandra S. Gersing, Sarah C. Foreman, Sophia S. Goller, Juergen Weissinger, Jon Rischewski, Anna-Sophia Dietrich, Benedikt Wiestler, Jan S. Kirschke, Ashkan Khakzar, Nassir Navab

Do black-box neural network models learn clinically relevant features for fracture diagnosis?

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

1 code implementation • 10 Mar 2021 • Hans Liebl, David Schinz, Anjany Sekuboyina, Luca Malagutti, Maximilian T. Löffler, Amirhossein Bayat, Malek El Husseini, Giles Tetteh, Katharina Grau, Eva Niederreiter, Thomas Baum, Benedikt Wiestler, Bjoern Menze, Rickmer Braren, Claus Zimmer, Jan S. Kirschke

With the advent of deep learning algorithms, fully automated radiological image analysis is within reach.

Anatomy Benchmarking +2