Visual Healthcare Computing
The research area “Visual Healthcare Computing” describes the application of computer vision, deep learning and visualization algorithms to the field of digital healthcare.
Examples are applications along a patient’s healthcare pathway, starting from image based diagnostics (e.g. based on endoscopy or digital pathology), endoscopy-based image enhancement for orientation and navigation (using e.g. 2D/3D panoramic imaging), computer assisted radiomics (in the field of mammography), contactless monitoring of vital signs (e.g. pulse and breathing) as well as interactive immersive training and planning systems for surgical interventions.
Automated polyp detection in the colorectum: a prospective study (with videos)
In: Gastrointestinal Endoscopy 89 (2019), p. 576-582.e1
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Digital Mapping of the Urinary Bladder: Potential for Standardized Cystoscopy Reports.
In: Urology 104 (2017), p. 235-241
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Using automated texture features to determine the probability for masking of a tumor on mammography, but not ultrasound
In: European Journal of Medical Research 22 (2017)
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Automated cancer stem cell recognition in H and E stained tissue using convolutional neural networks and color deconvolution
SPIE Medical Imaging 2017 (Orlando, Florida, USA)
In: Proceedings Volume 10140 Medical Imaging 2017:Digital Pathology 2017
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Automated plasmodia recognition in microscopic images for diagnosis of malaria using convolutional neural networks
Medical Imaging: Digital Pathology 2017
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