Babak Ehteshami Bejnordi

Affiliations: 
Radboud University Nijmegen, Nijmegen, Gelderland, Netherlands 
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Publications

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Veta M, Heng YJ, Stathonikos N, et al. (2019) Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge. Medical Image Analysis. 54: 111-121
Geessink OGF, Baidoshvili A, Klaase JM, et al. (2019) Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer Cellular Oncology. 42: 331-341
Litjens G, Bandi P, Bejnordi BE, et al. (2018) 1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset. Gigascience. 7
Bejnordi BE, Lin J, Glass B, et al. (2017) DEEP LEARNING-BASED ASSESSMENT OF TUMOR-ASSOCIATED STROMA FOR DIAGNOSING BREAST CANCER IN HISTOPATHOLOGY IMAGES. Proceedings. Ieee International Symposium On Biomedical Imaging. 2017: 929-932
Bejnordi BE, Zuidhof G, Balkenhol M, et al. (2017) Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images. Journal of Medical Imaging (Bellingham, Wash.). 4: 044504
Litjens G, Kooi T, Bejnordi BE, et al. (2017) A survey on deep learning in medical image analysis. Medical Image Analysis. 42: 60-88
Mullooly M, Bejnordi BE, Palakal M, et al. (2017) Abstract 4235: Application of convolutional neural networks to breast biopsies to uncover tissue correlates of mammographic breast density Cancer Research. 77: 4235-4235
Bejnordi BE, Litjens G, Timofeeva N, et al. (2016) Stain Specific Standardization of Whole-Slide Histopathological Images. Ieee Transactions On Medical Imaging. 35: 404-15
Bejnordi BE, Litjens G, Hermsen M, et al. (2015) A multi-scale superpixel classification approach to the detection of regions of interest in whole slide histopathology images Proceedings of Spie. 9420
Bejnordi BE, Timofeeva N, Otte-Höller I, et al. (2014) Quantitative analysis of stain variability in histology slides and an algorithm for standardization Proceedings of Spie. 9041: 904108
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