Research Group Michael Ingrisch
Michael Ingrisch
leads the group for Clinical Data Science at the Department of Radiology at LMU Munich.
His team employs advanced statistics, machine learning and computer vision techniques in the context of clinical radiology to enable fast and precise AI-supported diagnosis and prognostication. The research areas focus on applying computer vision techniques in radiology for diagnosis and prognosis, as well as using biostatistical methods to rigorously analyze clinical data. Additionally, the work includes leveraging large language models for clinical text analysis and developing multimodal deep learning models that integrate diverse data types, such as imaging and text, to improve AI model accuracy and applicability.
Team members @MCML
Publications @MCML
Automatische ICD-10-Codierung.
Die Radiologie 64 (Aug. 2024). DOI.
MCML Authors
Matthias Aßenmacher
Dr.
Statistical Learning & Data Science
ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports.
European Radiology 34 (May. 2024). DOI.
MCML Authors
Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition.
Preprint at arXiv (Apr. 2024). arXiv.
MCML Authors
Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction.
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024). Waikoloa, Hawaii, Jan 04-08, 2024. DOI.
MCML Authors
Bernd Bischl
Prof. Dr.
Statistical Learning & Data Science
Radiological age assessment based on clavicle ossification in CT: enhanced accuracy through deep learning.
International Journal of Legal Medicine (Jan. 2024). DOI.
MCML Authors
A comprehensive machine learning benchmark study for radiomics-based survival analysis of CT imaging data in patients with hepatic metastases of CRC.
Investigative Radiology 58.12 (Dec. 2023). DOI.
MCML Authors
Andreas Bender
Dr.
Statistical Learning & Data Science
Coordinator Statistical and Machine Learning Consulting
Bernd Bischl
Prof. Dr.
Statistical Learning & Data Science
Unreading Race: Purging Protected Features from Chest X-ray Embeddings.
Under review. Preprint at arXiv (Nov. 2023). arXiv.
MCML Authors
Bernd Bischl
Prof. Dr.
Statistical Learning & Data Science
Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis.
27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2023). Osaka, Japan, May 25-28, 2023. DOI.
Abstract
While recent advances in large-scale foundational models show promising results, their application to the medical domain has not yet been explored in detail. In this paper, we progress into the realms of large-scale modeling in medical synthesis by proposing Cheff - a foundational cascaded latent diffusion model, which generates highly-realistic chest radiographs providing state-of-the-art quality on a 1-megapixel scale. We further propose MaCheX, which is a unified interface for public chest datasets and forms the largest open collection of chest X-rays up to date. With Cheff conditioned on radiological reports, we further guide the synthesis process over text prompts and unveil the research area of report-to-chest-X-ray generation.
MCML Authors
Bernd Bischl
Prof. Dr.
Statistical Learning & Data Science
Implicit Embeddings via GAN Inversion for High Resolution Chest Radiographs.
1st Workshop on Medical Applications with Disentanglements (MAD 2022) at the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Singapore, Sep 18-22, 2022. DOI.
MCML Authors
Bernd Bischl
Prof. Dr.
Statistical Learning & Data Science
Survival-oriented embeddings for improving accessibility to complex data structures.
Workshop on Bridging the Gap: from Machine Learning Research to Clinical Practice at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021). Virtual, Dec 06-14, 2021. arXiv.
MCML Authors
Bernd Bischl
Prof. Dr.
Statistical Learning & Data Science
Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation.
Workshop on Deep Generative Models and Downstream Applications at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021). Virtual, Dec 06-14, 2021. PDF.
MCML Authors
Bernd Bischl
Prof. Dr.
Statistical Learning & Data Science
Bi-Centric Independent Validation of Outcome Prediction after Radioembolization of Primary and Secondary Liver Cancer.
Journal of Clinical Medicine 10.16 (Aug. 2021). DOI.
MCML Authors
Andreas Bender
Dr.
Statistical Learning & Data Science
Coordinator Statistical and Machine Learning Consulting