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Interpretable Vertebral Fracture Diagnosis

MCML Authors

Link to Profile Benedikt Wiestler PI Matchmaking

Benedikt Wiestler

Prof. Dr.

Principal Investigator

AI for Image-Guided Diagnosis and Therapy

Link to Profile Nassir Navab PI Matchmaking

Nassir Navab

Prof. Dr.

Principal Investigator

Computer Aided Medical Procedures & Augmented Reality

Abstract

Do black-box neural network models learn clinically relevant features for fracture diagnosis? The answer not only establishes reliability, quenches scientific curiosity, but also leads to explainable and verbose findings that can assist the radiologists in the final and increase trust. This work identifies the concepts networks use for vertebral fracture diagnosis in CT images. This is achieved by associating concepts to neurons highly correlated with a specific diagnosis in the dataset. The concepts are either associated with neurons by radiologists pre-hoc or are visualized during a specific prediction and left for the user’s interpretation. We evaluate which concepts lead to correct diagnosis and which concepts lead to false positives. The proposed frameworks and analysis pave the way for reliable and explainable vertebral fracture diagnosis.

inproceedings

iMIMIC @MICCAI 2022

Workshop on Interpretability of Machine Intelligence in Medical Image Computing at the 25th International Conference on Medical Image Computing and Computer Assisted Intervention. Singapore, Sep 18-22, 2022.

Authors

P. Engstler • M. Keicher • D. Schinz • K. Mach • A. S. Gersing • S. C. Foreman • S. S. Goller • J. Weissinger • J. Rischewski • A.-S. Dietrich • B. Wiestler • J. S. Kirschke • A. Khakzar • N. Navab

Links

DOI GitHub

Research Area

 C1 | Medicine

BibTeXKey: EKS+22

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