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Research Group Benedikt Wiestler

Link to Benedikt Wiestler

Benedikt Wiestler

Prof. Dr.

Principal Investigator

AI for Image-Guided Diagnosis and Therapy

Benedikt Wiestler

is Professor for AI for Image-Guided Diagnosis and Therapy at TU Munich.

His research bridges the gap between medicine and computer science towards data-driven, personalized medicine for diagnosis and therapy. His research focuses on developing innovative computational analysis methods to extract actionable biomarkers for clinical decision-making from heterogeneous, multi-modal medical data. Translating these advancements into clinical application is a core motivation for his work.

Publications @MCML

2024


[3]
B. Jian, J. Pan, M. Ghahremani, D. Rückert, C. Wachinger and B. Wiestler.
Mamba? Catch The Hype Or Rethink What Really Helps for Image Registration.
WBIR @MICCAI 2024 - 11th International Workshop on Biomedical Image Registration at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2024). Marrakesh, Morocco, Oct 06-10, 2024. DOI
Abstract

VoxelMorph, proposed in 2018, utilizes Convolutional Neural Networks (CNNs) to address medical image registration problems. In 2021 TransMorph advanced this approach by replacing CNNs with Attention mechanisms, claiming enhanced performance. More recently, the rise of Mamba with selective state space models has led to MambaMorph, which substituted Attention with Mamba blocks, asserting superior registration. These developments prompt a critical question: does chasing the latest computational trends with “more advanced” computational blocks genuinely enhance registration accuracy, or is it merely hype? Furthermore, the role of classic high-level registration-specific designs, such as coarse-to-fine pyramid mechanism, correlation calculation, and iterative optimization, warrants scrutiny, particularly in differentiating their influence from the aforementioned low-level computational blocks. In this study, we critically examine these questions through a rigorous evaluation in brain MRI registration. We employed modularized components for each block and ensured unbiased comparisons across all methods and designs to disentangle their effects on performance. Our findings indicate that adopting “advanced” computational elements fails to significantly improve registration accuracy. Instead, well-established registration-specific designs offer fair improvements, enhancing results by a marginal 1.5% over the baseline. Our findings emphasize the importance of rigorous, unbiased evaluation and contribution disentanglement of all low- and high-level registration components, rather than simply following the computer vision trends with “more advanced” computational blocks. We advocate for simpler yet effective solutions and novel evaluation metrics that go beyond conventional registration accuracy, warranting further research across various organs and modalities.

MCML Authors
Link to Bailiang Jian

Bailiang Jian

Artificial Intelligence in Radiology

Link to Morteza Ghahremani

Morteza Ghahremani

Dr.

Artificial Intelligence in Radiology

Link to Daniel Rückert

Daniel Rückert

Prof. Dr.

Artificial Intelligence in Healthcare and Medicine

Link to Christian Wachinger

Christian Wachinger

Prof. Dr.

Artificial Intelligence in Radiology

Link to Benedikt Wiestler

Benedikt Wiestler

Prof. Dr.

AI for Image-Guided Diagnosis and Therapy


[2]
M. Ghahremani, M. Khateri, B. Jian, B. Wiestler, E. Adeli and C. Wachinger.
H-ViT: A Hierarchical Vision Transformer for Deformable Image Registration.
CVPR 2024 - IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA, USA, Jun 17-21, 2024. DOI GitHub
Abstract

This paper introduces a novel top-down representation approach for deformable image registration, which estimates the deformation field by capturing various short-and long-range flow features at different scale levels. As a Hierarchical Vision Transformer (H-ViT), we propose a dual self-attention and cross-attention mechanism that uses high-level features in the deformation field to represent low-level ones, enabling information streams in the deformation field across all voxel patch embeddings irrespective of their spatial proximity. Since high-level features contain abstract flow patterns, such patterns are expected to effectively contribute to the representation of the deformation field in lower scales. When the self-attention module utilizes within-scale short-range patterns for representation, the cross-attention modules dynamically look for the key tokens across different scales to further interact with the local query voxel patches. Our method shows superior accuracy and visual quality over the state-of-the-art registration methods in five publicly available datasets, highlighting a substantial enhancement in the performance of medical imaging registration.

MCML Authors
Link to Morteza Ghahremani

Morteza Ghahremani

Dr.

Artificial Intelligence in Radiology

Link to Bailiang Jian

Bailiang Jian

Artificial Intelligence in Radiology

Link to Benedikt Wiestler

Benedikt Wiestler

Prof. Dr.

AI for Image-Guided Diagnosis and Therapy

Link to Christian Wachinger

Christian Wachinger

Prof. Dr.

Artificial Intelligence in Radiology


2022


[1]
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 and N. Navab.
Interpretable Vertebral Fracture Diagnosis.
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 (MICCAI 2022). Singapore, Sep 18-22, 2022. DOI GitHub
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.

MCML Authors
Link to Matthias Keicher

Matthias Keicher

Computer Aided Medical Procedures & Augmented Reality

Link to Benedikt Wiestler

Benedikt Wiestler

Prof. Dr.

AI for Image-Guided Diagnosis and Therapy

Link to Ashkan Khakzar

Ashkan Khakzar

Dr.

* Former member

Link to Nassir Navab

Nassir Navab

Prof. Dr.

Computer Aided Medical Procedures & Augmented Reality