N. Karssemeijer

Latest

• Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks
• H&E stain augmentation improves generalization of convolutional networks for histopathological mitosis detection
• Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
• Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images
• Using deep learning to segment breast and fibroglandular tissue in MRI volumes
• Large scale deep learning for computer aided detection of mammographic lesions
• Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities
• Automated Detection of DCIS in Whole-Slide H&E Stained Breast Histopathology Images
• Stain specific standardization of whole-slide histopathological images
• Clinical evaluation of a computer-aided diagnosis system for determining cancer aggressiveness in prostate MRI
• A multi-scale superpixel classification approach for region of interest detection in whole slide histopathology images
• Computer-aided detection of prostate cancer in MRI
• Computer-aided Detection of Prostate Cancer in Multi-parametric Magnetic Resonance Imaging
• Initial prospective evaluation of the prostate imaging reporting and data standard (PI-RADS): Can it reduce unnecessary MR guided biopsies?
• Automated computer-aided detection of prostate cancer in MR images: from a whole-organ to a zone-based approach
• A multi-atlas approach for prostate segmentation in MRI
• A pattern recognition approach to zonal segmentation of the prostate on MRI
• Computerized characterization of central gland lesions using texture and relaxation features from T2-weighted prostate MRI
• Automatic Computer Aided Detection of Abnormalities in Multi-Parametric Prostate MRI
• Zone-specific Automatic Computer-aided Detection of Prostate Cancer in MRI
• Training a Computer Aided Detection System with Simulated Lung Nodules in Chest Radiographs