MIDL 2021

 
International Conference on
Medical Imaging with Deep Learning
5. bis 7. Juli 2021

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T
Yang C., Indurkhya B., See J., Grzegorzek M.
Towards Automatic Skeleton Extraction with Skeleton Grafting
IEEE Transactions on Visualization and Computer Graphics, 1, 2020
S
Wilms M., Werner R., Yamamoto T., Handels H., Ehrhardt J.
Subpopulation-based correspondence modelling for improved respiratory motion estimation in the presence of inter-fraction motion variations
Physics in Medicine and Biology, 62, 14, 5823-5839, 2017
Suinesiaputra A., Ablin P., Alba X., Alessandrini M., Allen J., Bai W., Cimen S., Claes P., Cowan B.R., D'hooge J. et al.
Statistical shape modeling of the left ventricle: myocardial infarct classification challenge.
IEEE journal of biomedical and health informatics, 22, 2, 503-515, 2018
Abstract
Wilms M., Ehrhardt J., Werner R., Marx M., Handels H.
Statistical Analysis of Surrogate Signals to Incorporate Respiratory Motion Variability into Radiotherapy Treatment Planning
In: Yaniv Z.R., Holmes III D.R. (eds.), SPIE Medical Imaging 2014, Image-Guided Procedures, Robotic Interventions, and Modeling, San Diego, USA, 9036, 90360J, 2014
Ai S., Li C., Li X., Jiang T., Grzegorzek M., Sun C., Rahaman M.M., Zhang J., Yao Y., Li H.
A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development
BioMed Research International, Hindawi, 2021
R
Yang C., Flak O., Grzegorzek M.
Representation and Matching of Team Managers: An Experimental Research
IEEE Transactions on Computational Social Systems, 5, 2, 311-323, 2018
P
Wilms M., Werner R., Yamamoto T., Handels H., Ehrhardt J.
Population-based Correspondence Models for Respiratory Motion Estimation in the Presence of Interfraction Motion Variations
In: Imaging and Computer Assistance in Radiation Therapy, ICART 2015, 18th International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, München, 81-88, 2015
PDF
O
Hempe H., Yilmaz E.B., Meyer C., Heinrich M.P.
Opportunistic CT screening for degenerative deformities and osteoporotic fractures with 3D DeepLab
In: Proc. SPIE 12032, Medical Imaging 2022: Image Processing, San Diego, United States, SPIE, 17, 2022
E
Zhuang X., Li L., Payer C., Štern D., Urschler M., Heinrich M.P., Oster J., Wang C., Smedby Ö., Bian C. et al.
Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge
Medical Image Analysis, 58, 101537, 2019
C
Zhang J., Li C., Rahaman M.M., Yao Y., Ma P., Zhang J., Zhao X., Jiang T., Grzegorzek M.
A Comprehensive Review of Image Analysis Methods for Microorganism Counting: From Classical Image Processing to Deep Learning Approaches
Artificial Intelligence Review, Springer, 2021
"
Oberländer M., Linnebacher M., König A., Bogoevska V., Brodersen C., Kaatz R., Krohn M., Hackmann M., Ingenerf J., Christoph J. et al.
The "North German Tumor Bank of Colorectal Cancer": Status Report after the First 2 Years of Support by the German Cancer Aid Foundation
Langenbeck's archives of surgery / Deutsche Gesellschaft für Chirurgie, 398, 2, 251-8, 2013
Abstract

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