Medizinische Bild- und Signalverarbeitung


zum Themenbereich Medizinische Bild- und Signalverarbeitung


Von der AG mit organisiert oder unterstützt

VCBM (Visual Computing in Biology and Medicine) Workshop 
20. und 21. September 2018 - Der Workshop ist damit räumlich und zeitlich in unmittelbarer Nähe zur MICCAI.
„Visual Computing“ steht für die Integration von Bildanalyse, Visualisierung und Interaktion“.
Der beiliegende Flyer und die Website  enthalten detailliertere Informationen."

Workshop Bildverarbeitung für die Medizin (BVM)
turnus: jährlich
nächster Workshop: Frühjahr 2019, Lübeck, Link

Biomedical Image and Signal Computing (BISC)
gemeinsamer Workshop zusammen mit der DGBMT

1. BISC im Rahmen der GMDS Jahrestagung in Lübeck, 2013, Link
2. BISC als Focussession im Rahmen der BMT in Lübeck, 2015, Link


Jahrestagung der GMDS
17. September 2017 bis 21. September 2017 in Oldenburg Link

Jahrestagung der GI
25. September 2017 bis 29. September 2017 in Chemnitz Link

weitere Hinweise

The First International Workshop on Thoracic Image Analysis
A MICCAI 2018 Workshop

The Workshop on Thoracic Image Analysis (TIA) brings together medical image analysis researchers in the area of thoracic imaging to discuss recent advances in this rapidly developing field. Cardiovascular disease, lung cancer and COPD, three diseases all visible on thoracic imaging, are amongst the top causes of death worldwide. Many imaging modalities are currently available to study the pulmonary and cardiac system, including radiography, CT, PET and MRI. We invite papers that deal with all aspects of image analysis of thoracic data. Further, we particularly welcome independent validation studies on the use of deep learning models in the area of thoracic imaging as well as live demonstrations of software.

Paper submission deadline: June 18, 2018
Workshop date: September 20, 2018



Blending Visualization with Data Mining and Machine Learning for Biomedical Data Analysis

In the tutorial, we address the blending of visualization with data mining and machine learning, in particular, deep learning, from a research- and an application-oriented perspective. We show how visualization assists in understanding high-dimensional parameter spaces and cluster structure, in the understanding of learned features, as well as in the tailor-made design and improvement of neural networks. We demonstrate applications in cardiac surgery planning, understanding gene-structure behavior in neurosciences, tumor tissue characterization, risk factor identification in epidemiology, and clinical decision support.

Date: 16. September, 2018



Digital Therapy and Patient Models for Clinical Decision Support

In the tutorial, we present two complementary approaches to building predictive disease- and patient-specific therapeutic decision models supporting medical experts. First, we describe techniques for building a probabilistic therapy model which represents weighted causalities between patient information aggregated from medical health records and knowledge derived from medical textbooks, clinical studies, and therapeutic guidelines. Second, we detail techniques for building a physiological patient model which represents the function of an anatomical structure as well as therapy-induced functional variations, both derived from medical images and image-based, patient-specific simulations. Finally, we elaborate on an integration of both models.

Date: 16. September, 2018