Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer

Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes van Diest, +65

JAMA

Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

Geert Litjens, Clara I. Sá‎nchez, N. K. Timofeeva, +6

Scientific Reports

From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge

Péter Bándi, Oscar Geessink, Quirine F. Manson, +33

IEEE Transactions on Medical Imaging

1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset

Geert Litjens, Péter Bándi, Babak Ehteshami Bejnordi, +14

GigaScience

Deep Learning–Based Histopathologic Assessment of Kidney Tissue

Meyke Hermsen, Thomas de Bel, Marjolijn den Boer, +10

Journal of the American Society of Nephrology

Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images

Babak Ehteshami Bejnordi, Guido Zuidhof, Maschenka Balkenhol, +6

Journal of Medical Imaging

Stain-Transforming Cycle-Consistent Generative Adversarial Networks for Improved Segmentation of Renal Histopathology

Thomas de Bel, Meyke Hermsen, Jesper Kers, +2

Data Archiving and Networked Services (DANS)

Evaluation of p16/Ki-67 dual-stained cytology as triage test for high-risk human papillomavirus-positive women

Renée M.F. Ebisch, Judith van der Horst, Meyke Hermsen, +13

Modern Pathology

Cancer DetectionHuman Papillomavirus VaccinesMedicineDiagnostic TestGynecology +8

Automatic segmentation of histopathological slides of renal tissue using deep learning

Thomas de Bel, Meyke Hermsen, Jeroen van der Laak, +3

EngineeringRenal PathologyDigital PathologyPathologyBiomedical Engineering +18

A multi-scale superpixel classification approach to the detection of regions of interest in whole slide histopathology images

Babak Ehteshami Bejnordi, Geert Litjens, Meyke Hermsen, +2

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE

EngineeringDigital PathologyImage ClassificationImage AnalysisPattern Recognition +16