IEEE Symposium on Computational Intelligence in Computer Assisted Analysis and Clinical Diagnosis (IEEE CAACD)
The 2019 IEEE Symposium on Computational Intelligence in Computer Assisted Analysis and Clinical Diagnosis (CAACD’19) will be held in a very beautiful port city, Xiamen, China, during December 6-9, 2019. CIHE’19 is part of the 2019 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2019, http://www.ssci2019.org/), a flagship annual international conference of the IEEE Computational Intelligence Society (CIS).
Due to the highly developed medical imaging technology and the significantly enhanced computing power, computer assisted clinical data analysis and diagnosis have been intensively studied. In the last decade, many different image processing approaches and machine learning methods combined with a large amount of clinical data have been proposed to fulfil many challenge clinical tasks, such as segmentation (e.g., nodule segmentation), registration (e.g., brain image registration), diagnosis (e.g., AD/MCI classification), treatment (e.g., surgical navigation) and prognosis (e.g., overall survival time prediction). The ultimate goal is to release physicians from labor-intensive tasks, such as manually labeling, and provide physicians with important information to realize personalized treatment. This symposium aims to build an interdisciplinary communication platform for both physicians and computer scientists to present their own perspective about the computer assisted clinical data analysis and diagnosis, and also for pursuing the new trend of image processing and machine learning technology for clinic data.
This symposium welcomes original, unpublished contributions from authors. Topics include (but not limited):
- Methods for medical image processing (segmentation, registration, etc.)
- Machine learning and artificial intelligence using clinical data
- Computer assisted diagnosis
- Reconstruction and visualization
- Image-Guided Interventions and Surgery
- Interventional Tracking and Navigation
- Surgical Planning and Simulation
- Applications of big data in clinic
- Personalized treatment