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The ability to automatically detect certain types of cells or cellular subunits in microscopy images is of significant interest to a wide range of biomedical research and clinical practices.
Solving multiclass learning problems via error-correcting output codes
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Cell segmentation: 50 years down the road [life sciences]
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Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks
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Deep residual learning for image recognition
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Faster R-CNN: Towards real-time object detection with region proposal networks
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Assessment of algorithms for mitosis detection in breast cancer histopathology images
Mitko Veta, Paul J van Diest, Stefan M Willems, Haibo Wang, Anant Madabhushi, Angel Cruz-Roa, Fabio Gonzalez, Anders B L Larsen, Jacob S Vestergaard, Anders B Dahl, Dan C Cireşan, Jürgen Schmidhuber, Alessandro Giusti, Luca M Gambardella, F Boray Tek, Thomas Walter, Ching-Wei Wang, Satoshi Kondo, Bogdan J Matuszewski, Frederic Precioso, Violet Snell, Josef Kittler, Teofilo E de Campos, Adnan M Khan, Nasir M Rajpoot, Evdokia Arkoumani, Miangela M Lacle, Max A Viergever, and Josien P W Pluim · 2015
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Microscopy cell counting with fully convolutional regression networks
Weidi Xie, J. Alison Noble, and Andrew Zisserman · 2015
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Classification of mitotic figures with convolutional neural networks and seeded blob features
Christopher D. Malon and Eric Cosatto · 2013
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Mitosis detection in breast cancer histological images, an icpr 2012 contest
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Mitosis detection in breast cancer histology images via deep cascaded networks
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Exploiting random projections and sparsity with random forests and gradient boosting methods
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Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images
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Aggnet: Deep learning from crowds for mitosis detection in breast cancer histology images
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Fully convolutional models for semantic segmentation
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Cell counting by regression using convolutional neural network
Yao Xue, Nilanjan Ray, Judith Hugh, and Gilbert Bigras · 2016
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