13.09.2022

Teaser image to

Five papers at MICCAI 2022

25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Singapore, 18.09.2022–22.09.2022

We are happy to announce that MCML researchers are represented with five papers at MICCAI 2022:

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 website

Matthias Keicher

Computer Aided Medical Procedures & Augmented Reality

Link to Profile Benedikt Wiestler

Benedikt Wiestler

Prof. Dr.

AI for Image-Guided Diagnosis and Therapy

Link to website

Ashkan Khakzar

Dr.

* Former member

Link to Profile Nassir Navab

Nassir Navab

Prof. Dr.

Computer Aided Medical Procedures & Augmented Reality


A. Farshad, A. Makarevich, V. Belagiannis and N. Navab.
MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation.
DART @MICCAI 2022 - 4th Workshop on Domain Adaptation and Representation Transfer at the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Singapore, Sep 18-22, 2022. DOI GitHub
Abstract

The lack of sufficient annotated image data is a common issue in medical image segmentation. For some organs and densities, the annotation may be scarce, leading to poor model training convergence, while other organs have plenty of annotated data. In this work, we present MetaMedSeg, a gradient-based meta-learning algorithm that redefines the meta-learning task for the volumetric medical data with the goal of capturing the variety between the slices. We also explore different weighting schemes for gradients aggregation, arguing that different tasks might have different complexity and hence, contribute differently to the initialization. We propose an importance-aware weighting scheme to train our model. In the experiments, we evaluate our method on the medical decathlon dataset by extracting 2D slices from CT and MRI volumes of different organs and performing semantic segmentation. The results show that our proposed volumetric task definition leads to up to improvement in terms of IoU compared to related baselines. The proposed update rule is also shown to improve the performance for complex scenarios where the data distribution of the target organ is very different from the source organs.

MCML Authors
Link to website

Azade Farshad

Dr.

Computer Aided Medical Procedures & Augmented Reality

Link to Profile Nassir Navab

Nassir Navab

Prof. Dr.

Computer Aided Medical Procedures & Augmented Reality


A. Farshad, Y. Yeganeh, P. Gehlbach and N. Navab.
Y-Net: A Spatiospectral Dual-Encoder Network for Medical Image Segmentation.
MICCAI 2022 - 25th International Conference on Medical Image Computing and Computer Assisted Intervention. Singapore, Sep 18-22, 2022. DOI GitHub
Abstract

Automated segmentation of retinal optical coherence tomography (OCT) images has become an important recent direction in machine learning for medical applications. We hypothesize that the anatomic structure of layers and their high-frequency variation in OCT images make retinal OCT a fitting choice for extracting spectral domain features and combining them with spatial domain features. In this work, we present Y-Net, an architecture that combines the frequency domain features with the image domain to improve the segmentation performance of OCT images. The results of this work demonstrate that the introduction of two branches, one for spectral and one for spatial domain features, brings very significant improvement in fluid segmentation performance and allows outperformance as compared to the well-known U-Net model. Our improvement was 13% on the fluid segmentation dice score and 1.9% on the average dice score. Finally, removing selected frequency ranges in the spectral domain demonstrates the impact of these features on the fluid segmentation outperformance.

MCML Authors
Link to website

Azade Farshad

Dr.

Computer Aided Medical Procedures & Augmented Reality

Link to website

Yousef Yeganeh

Computer Aided Medical Procedures & Augmented Reality

Link to Profile Nassir Navab

Nassir Navab

Prof. Dr.

Computer Aided Medical Procedures & Augmented Reality


T. Weber, M. Ingrisch, B. Bischl and D. Rügamer.
Implicit Embeddings via GAN Inversion for High Resolution Chest Radiographs.
MAD @MICCAI 2022 - 1st Workshop on Medical Applications with Disentanglements at the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Singapore, Sep 18-22, 2022. DOI
Abstract

Generative models allow for the creation of highly realistic artificial samples, opening up promising applications in medical imaging. In this work, we propose a multi-stage encoder-based approach to invert the generator of a generative adversarial network (GAN) for high resolution chest radiographs. This gives direct access to its implicitly formed latent space, makes generative models more accessible to researchers, and enables to apply generative techniques to actual patient’s images. We investigate various applications for this embedding, including image compression, disentanglement in the encoded dataset, guided image manipulation, and creation of stylized samples. We find that this type of GAN inversion is a promising research direction in the domain of chest radiograph modeling and opens up new ways to combine realistic X-ray sample synthesis with radiological image analysis.

MCML Authors
Link to Profile Michael Ingrisch

Michael Ingrisch

Prof. Dr.

Clinical Data Science in Radiology

Link to Profile Bernd Bischl

Bernd Bischl

Prof. Dr.

Statistical Learning & Data Science

Link to Profile David Rügamer

David Rügamer

Prof. Dr.

Data Science Group


Y. Yeganeh, A. Farshad, J. Boschmann, R. Gaus, M. Frantzen and N. Navab.
FedAP: Adaptive Personalization in Federated Learning for Non-IID Data.
DeCaF FAIR @MICCAI 2022 - 3rd Workshop on Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health at the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Singapore, Sep 18-22, 2022. DOI
Abstract

Federated learning (FL) is a distributed learning method that offers medical institutes the prospect of collaboration in a global model while preserving the privacy of their patients. Although most medical centers conduct similar medical imaging tasks, their differences, such as specializations, number of patients, and devices, lead to distinctive data distributions. Data heterogeneity poses a challenge for FL and the personalization of the local models. In this work, we investigate an adaptive hierarchical clustering method for FL to produce intermediate semi-global models, so clients with similar data distribution have the chance of forming a more specialized model. Our method forms several clusters consisting of clients with the most similar data distributions; then, each cluster continues to train separately. Inside the cluster, we use meta-learning to improve the personalization of the participants’ models. We compare the clustering approach with classical FedAvg and centralized training by evaluating our proposed methods on the HAM10k dataset for skin lesion classification with extreme heterogeneous data distribution. Our experiments demonstrate significant performance gain in heterogeneous distribution compared to standard FL methods in classification accuracy. Moreover, we show that the models converge faster if applied in clusters and outperform centralized training while using only a small subset of data.

MCML Authors
Link to website

Yousef Yeganeh

Computer Aided Medical Procedures & Augmented Reality

Link to website

Azade Farshad

Dr.

Computer Aided Medical Procedures & Augmented Reality

Link to Profile Nassir Navab

Nassir Navab

Prof. Dr.

Computer Aided Medical Procedures & Augmented Reality


13.09.2022


Subscribe to RSS News feed

Related

Link to

05.12.2024

26 papers at NeurIPS 2024

38th Conference on Neural Information Processing Systems (NeurIPS 2024). Vancouver, Canada, 10.12.2024 - 15.12.2024


Link to

06.11.2024

20 papers at EMNLP 2024

Conference on Empirical Methods in Natural Language Processing (EMNLP 2024). Miami, FL, USA, 12.11.2024 - 16.11.2024


Link to

18.10.2024

Three papers at ECAI 2024

27th European Conference on Artificial Intelligence (ECAI 2024). Santiago de Compostela, Spain, 19.10.2024 - 24.10.2024


Link to

01.10.2024

16 papers at MICCAI 2024

27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2024). Marrakesh, Morocco, 06.10.2024 - 10.10.2024


Link to

26.09.2024

20 papers at ECCV 2024

18th European Conference on Computer Vision (ECCV 2024). Milano, Italy, 29.09.2024 - 04.10.2024