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The rapidly emerging field of computational pathology has the potential to enable objective diagnosis, therapeutic response prediction and identification of new morphological features of clinical relevance.
A framework for multiple-instance learning
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Multiple instance boosting for object detection
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Systematic analysis of breast cancer morphology uncovers stromal features associated with survival
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Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling
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Deep learning
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Classifying and segmenting microscopy images with deep multiple instance learning
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histocat: analysis of cell phenotypes and interactions in multiplex image cytometry data
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Comprehensive and integrated genomic characterization of adult soft tissue sarcomas
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Dermatologist-level classification of skin cancer with deep neural networks
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Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
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Adversarial stain transfer for histopathology image analysis
BenTaieb, A. & Hamarneh, G · 2017
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Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images
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Predicting cancer outcomes from histology and genomics using convolutional networks
Mobadersany, P. et al · 2018
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Microenvironmental niche divergence shapes brca1-dysregulated ovarian cancer morphological plasticity
Heindl, A. et al · 2018
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Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
Poplin, R. et al · 2018
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Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
Coudray, N. et al · 2018
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Attention-based deep multiple instance learning
Ilse, M., Tomczak, J. & Welling, M · 2018
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Multiple instance learning for heterogeneous images: Training a cnn for histopathology
Couture, H. D., Marron, J. S., Perou, C. M., Troester, M. A. & Niethammer, M · 2018
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Smooth loss functions for deep top-k classification
Automated acquisition of explainable knowledge from unannotated histopathology images
Yamamoto, Y. et al · 2019
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The use of digital pathology and image analysis in clinical trials
Pell, R. et al · 2019
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A guide to deep learning in healthcare
Esteva, A. et al · 2019
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Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning
Shen, L., Zhao, W. & Xing, L · 2019
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An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis
Chen, P.-H. C. et al · 2019
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Development and validation of a deep learning algorithm for improving gleason scoring of prostate cancer
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Berrada, L., Zisserman, A. & Kumar, M. P · 2018
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1399 h&e-stained sentinel lymph node sections of breast cancer patients: the camelyon dataset
Litjens, G. et al · 2018
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Artificial intelligence in digital pathology—new tools for diagnosis and precision oncology
Bera, K., Schalper & Madabhushi, A · 2019
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Digital pathology and artificial intelligence
Niazi, M. K. K., Parwani, A. V. & Gurcan, M. N · 2019
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Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer
Kather, J. N. et al · 2019
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Deep learning for cellular image analysis
Moen, E. et al · 2019
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Deep adversarial training for multi-organ nuclei segmentation in histopathology images
Mahmood, F. et al · 2019
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Nagpal, K. et al · 2019
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Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Campanella, G. et al · 2019
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Near real-time intraoperative brain tumor diagnosis using stimulated raman histology and deep neural networks
Hollon, T. C. et al · 2020
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Automated deep-learning system for gleason grading of prostate cancer using biopsies: a diagnostic study
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Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study
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International evaluation of an ai system for breast cancer screening
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