Deep learning in microscopy image analysis: a survey

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works

Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · see citing works

Generative Adversarial Nets

Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,100 · see citing works

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works

Auto-Encoding Variational Bayes

Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · see citing works

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works

A fast learning algorithm for deep belief nets

Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336 · see citing works

Visualizing and understanding convolutional networks

Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works

A survey on deep learning in medical image analysis

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works

Representation Learning: A Review and New Perspectives

Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works

Learning from Imbalanced Data

He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359 · see citing works

Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups

Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260 · see citing works

Intriguing properties of neural networks

Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv · doi:10.48550/arxiv.1312.6199 · Cited by 8,143 · see citing works

Learning Deep Architectures for AI

Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,048 · see citing works

How transferable are features in deep neural networks?

Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061 · see citing works

Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning

Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759 · see citing works

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370 · see citing works

Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0981-7 · Cited by 5,107 · see citing works

Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition

Pati, Y., Rezaiifar, R., & Krishnaprasad, P. (1993) · Proceedings of 27th Asilomar Conference on Signals, Systems and Computers · doi:10.1109/acssc.1993.342465 · Cited by 4,928 · see citing works

Greedy layer-wise training of deep networks

Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841 · see citing works

Caffe: Convolutional Architecture for Fast Feature EmbeddingPreprint

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5093 · Cited by 4,749 · see citing works

An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision

Boykov, Y., & Kolmogorov, V. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.60 · Cited by 4,577 · see citing works

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130140 · Cited by 4,524 · see citing works

VQA: Visual Question Answering

Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.279 · Cited by 4,487 · see citing works

CNN Features Off-the-Shelf: An Astounding Baseline for Recognition

Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2014.131 · Cited by 4,451 · see citing works

Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051 · see citing works

Multi-column deep neural networks for image classification

Ciresan, D. C., Meier, U., & Schmidhuber, J. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248110 · Cited by 3,760 · see citing works

Deep Learning: Methods and Applications

Deng, L., & Yu, D. (2014) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000039 · Cited by 3,480 · see citing works

Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535302 · Cited by 3,277 · see citing works

Large Scale Distributed Deep Networks

Dean, J. B., Corrado, G. S., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M., Senior, A., Tucker, P. A., Yang, K., et al. (2012) · Cited by 3,189 · see citing works

Neural networks and Deep Learning

Theodoridis, S. (2016) · Machine Learning Guide for Oil and Gas Using Python · doi:10.1017/cbo9781316576533.019 · Cited by 2,792 · see citing works

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., & Langs, G. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-59050-9_12 · Cited by 2,627 · see citing works

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553453 · Cited by 2,412 · see citing works

Convolutional networks and applications in vision

LeCun, Y., Kavukcuoglu, K., & Farabet, C. (2010) · Proceedings of 2010 IEEE International Symposium on Circuits and Systems · doi:10.1109/iscas.2010.5537907 · Cited by 2,213 · see citing works

Why Does Unsupervised Pre-training Help Deep Learning?

Erhan, D., Bengio, Y., Courville, A. C., Manzagol, P.-A., Vincent, P., & Samy Bengio, A. (2010) · Cited by 2,180 · see citing works

Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks

Zhang, C., Li, P., Sun, G., Guan, Y., Xiao, B., & Cong, J. (2015) · Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays · doi:10.1145/2684746.2689060 · Cited by 2,158 · see citing works

Histopathological Image Analysis: A Review

Gurcan, M. N., Boucheron, L. E., Can, A., Madabhushi, A., Rajpoot, N. M., & Yener, B. (2009) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2009.2034865 · Cited by 2,052 · see citing works

Towards an integrated crowdsourcing definition

Estellés-Arolas, E., & González-Ladrón-de-Guevara, F. (2012) · Journal of Information Science · doi:10.1177/0165551512437638 · Cited by 1,859 · see citing works

Understanding deep image representations by inverting them

Mahendran, A., & Vedaldi, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299155 · Cited by 1,827 · see citing works

The foundations of cost-sensitive learning

Elkan, C. (2001) · Cited by 1,812 · see citing works

Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

Greenspan, H. K., van Ginneken, B., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2553401 · Cited by 1,704 · see citing works

Scalable Parallel Programming with CUDA

Nickolls, J. R., Buck, I., Garland, M., & Kevin Skadron, Y. (2008) · Queue · doi:10.1145/1365490.1365500 · Cited by 1,666 · see citing works

Deeply-Supervised NetsPreprint

Lee, C., Xie, S., Gallagher, P., Zhang, Z., & Tu, Z. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.5185 · Cited by 1,661 · see citing works

Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks

Cireşan, D. C., Giusti, A., Gambardella, L. M., & Schmidhuber, J. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_51 · Cited by 1,536 · see citing works

Deep learning in bioinformatics

Min, S., Lee, B., & Yoon, S. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw068 · Cited by 1,514 · see citing works

Training cost-sensitive neural networks with methods addressing the class imbalance problem

Zhou, Z., & Liu, X. (2006) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2006.17 · Cited by 1,354 · see citing works

Untitled work

(2012) · neural information processing systems · Cited by 1,291 · see citing works

Going Deeper with Embedded FPGA Platform for Convolutional Neural Network

Qiu, J., Wang, J., Yao, S., Guo, K., Li, B., Zhou, E., Yu, J., Tang, T., Xu, N., Song, S., et al. (2016) · ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA) · doi:10.1145/2847263.2847265 · Cited by 1,268 · see citing works

Quantized Convolutional Neural Networks for Mobile Devices

Wu, J., Leng, C., Wang, Y., Hu, Q., & Cheng, J. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.521 · Cited by 1,244 · see citing works

Torch7: A Matlab-like Environment for Machine Learning

Collobert, R., Kavukcuoglu, K., & Farabet, C. (2011) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 1,241 · see citing works

A Framework for Multiple-Instance Learning

Maron, O., & Lozano‐Pérez, T. (1997) · Cited by 1,232 · see citing works

Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images

Sirinukunwattana, K., Raza, S. E. A., Tsang, Y.-W., Snead, D. R. J., Cree, I. A., & Rajpoot, N. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2525803 · Cited by 1,224 · see citing works

Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent

Niu, F., Recht, B., Re, C., & Wright, S. J. (2011) · arXiv · doi:10.48550/arxiv.1106.5730 · Cited by 1,211 · see citing works

A Theoretical Analysis of Feature Pooling in Visual Recognition

Boureau, Y.-L., Ponce, J., & LeCun, Y. (2010) · Cited by 1,196 · see citing works

Evaluating the Visualization of What a Deep Neural Network Has Learned

Samek, W., Binder, A., Montavon, G., Lapuschkin, S., & Muller, K.-R. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2599820 · Cited by 1,164 · see citing works

Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships

Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E., & Svetnik, V. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500747n · Cited by 1,159 · see citing works

Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]

Arel, I., Rose, D. C., & Karnowski, T. P. (2010) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2010.938364 · Cited by 1,128 · see citing works

Selective Sampling Using the Query by Committee Algorithm

Freund, Y., Seung, H. S., Shamir, E., & Tishby, N. (1997) · Machine Learning · doi:10.1023/a:1007330508534 · Cited by 1,098 · see citing works

Image analysis and machine learning in digital pathology: challenges and opportunities

Madabhushi, A., & Lee, G. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.06.037 · Cited by 1,093 · see citing works

EIE: Efficient Inference Engine on Compressed Deep Neural Network

Han, S., Liu, X., Mao, H., Pu, J., Pedram, A., Horowitz, M. A., & Dally, W. J. (2016) · 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA) · doi:10.1109/isca.2016.30 · Cited by 1,077 · see citing works

Learning To Count Objects in Images

Lempitsky, V., & Zisserman, A. (2010) · Cited by 1,063 · see citing works

Scaling Distributed Machine Learning with the Parameter Server

Li, M. (2014) · International Conference on Big Data Science and Computing (BigDataScience) · doi:10.1145/2640087.2644155 · Cited by 1,000 · see citing works

Imbalanced Learning: Foundations, Algorithms, and Applications

He, H.-B., & Ma, Y. (2013) · Wiley eBooks · doi:10.1002/9781118646106 · Cited by 917 · see citing works

Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification

Hou, L., Samaras, D., Kurç, T., Gao, Y., Davis, J., & Saltz, J. H. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.266 · Cited by 902 · see citing works

On optimization methods for deep learning

Ngiam, J., Coates, A., Lahiri, A., Prochnow, B., Le, Q. V., & Ng, A. Y. (2011) · International Conference on Machine Learning · Cited by 868 · see citing works

Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images

Xu, J., Xiang, L., Liu, Q., Gilmore, H., Wu, J., Tang, J., & Madabhushi, A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2458702 · Cited by 854 · see citing works

Deep Direct Reinforcement Learning for Financial Signal Representation and Trading

Deng, Y., Bao, F., Kong, Y., Ren, Z., & Dai, Q. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2522401 · Cited by 822 · see citing works

Representational Power of Restricted Boltzmann Machines and Deep Belief Networks

Roux, N. L., & Bengio, Y. (2008) · Neural Computation · doi:10.1162/neco.2008.04-07-510 · Cited by 810 · see citing works

Sparse Feature Learning for Deep Belief Networks

Ranzato, M., Boureau, Y.-L., & LeCun, Y. (2007) · neural information processing systems · Cited by 752 · see citing works

Medical Image Synthesis with Context-Aware Generative Adversarial Networks

Nie, D., Trullo, R., Lian, J., Petitjean, C., Ruan, S., Wang, Q., & Shen, D. G. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66179-7_48 · Cited by 751 · see citing works

Large-scale deep unsupervised learning using graphics processors

Raina, R., Madhavan, A., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553486 · Cited by 742 · see citing works

14.5 Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks

Chen, Y., Krishna, T., Emer, J., & Sze, V. (2016) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc.2016.7418007 · Cited by 713 · see citing works

Breast Cancer Histopathology Image Analysis: A Review

Veta, M., Pluim, J. P. W., van Diest, P. J., & Viergever, M. A. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2303852 · Cited by 696 · see citing works

Multiple Instance Boosting for Object Detection

Zhang, C., Platt, J. C., & Viola, P. A. (2005) · neural information processing systems · Cited by 692 · see citing works

Untitled work

(2013) · Cited by 651 · see citing works

Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential

Irshad, H., Veillard, A., Roux, L., & Racoceanu, D. (2014) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2013.2295804 · Cited by 641 · see citing works

Ask Your Neurons: A Neural-Based Approach to Answering Questions about Images

Malinowski, M., Rohrbach, M., & Fritz, M. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.9 · Cited by 633 · see citing works

AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images

Albarqouni, S., Baur, C., Achilles, F., Belagiannis, V., Demirci, S., & Navab, N. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528120 · Cited by 606 · see citing works

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

Xing, F., & Yang, L. (2016) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2016.2515127 · Cited by 518 · see citing works

A Nonlinear Mapping Approach to Stain Normalization in Digital Histopathology Images Using Image-Specific Color Deconvolution

Khan, A. M., Rajpoot, N., Treanor, D. E., & Magee, D. R. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2303294 · Cited by 509 · see citing works

On the Complexity of Neural Network Classifiers: A Comparison Between Shallow and Deep Architectures

Bianchini, M., & Scarselli, F. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2293637 · Cited by 500 · see citing works

Assessment of algorithms for mitosis detection in breast cancer histopathology images

Veta, M., van Diest, P. J., Willems, S. M., Wang, H., Madabhushi, A., Cruz-Roa, Á., Gonzalez, F. A., Larsen, A., Vestergaard, J. S., Dahl, A. B., et al. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.11.010 · Cited by 480 · see citing works

Cell Segmentation: 50 Years Down the Road [Life Sciences]

Meijering, E. H. W. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2204190 · Cited by 474 · see citing works

Deep Learning in Label-free Cell Classification

Chen, C. L., Mahjoubfar, A., Tai, L., Blaby, I. K., Huang, A., Niazi, K., & Jalali, B. (2016) · Scientific Reports · doi:10.1038/srep21471 · Cited by 449 · see citing works

Minerva: enabling low-power, highly-accurate deep neural network accelerators

Reagen, B., Whatmough, P., Adolf, R., Rama, S., Lee, H., Lee, S. K., Hernández-Lobato, J. M., Wei, G.-Y., & Brooks, D. (2016) · ACM SIGARCH Computer Architecture News · doi:10.1145/3007787.3001165 · Cited by 446 · see citing works

Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene

Li, J., Mei, X., Prokhorov, D. V., & Tao, D. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2522428 · Cited by 437 · see citing works

Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images

van Grinsven, M. J. J. P., van Ginneken, B., Hoyng, C. B., Theelen, T., & Sanchez, C. I. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2526689 · Cited by 426 · see citing works

A benchmark for comparison of cell tracking algorithms

Maška, M., Ulman, V., Svoboda, D., Matula, P., Matula, P., Ederra, C., Urbiola, A., España, T., Venkatesan, S., Balak, D. M. W., et al. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu080 · Cited by 415 · see citing works

A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection

Cruz-Roa, A. A., Arevalo Ovalle, J. E., Madabhushi, A., & González Osorio, F. A. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_50 · Cited by 403 · see citing works

Unsupervised learning of hierarchical representations with convolutional deep belief networks

Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2011) · Communications of the ACM · doi:10.1145/2001269.2001295 · Cited by 386 · see citing works

Deep learning of feature representation with multiple instance learning for medical image analysis

Xu, Y. L., Mo, T., Feng, Q., Zhong, P., Lai, M., & Chang, E. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6853873 · Cited by 385 · see citing works

Scene labeling with LSTM recurrent neural networks

Byeon, W., Breuel, T. M., Raue, F., & Liwicki, M. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298977 · Cited by 384 · see citing works

Minimizing Computation in Convolutional Neural Networks

Cong, J., & Xiao, B. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11179-7_36 · Cited by 379 · see citing works

Efficient Parallel Framework for HEVC Motion Estimation on Many-Core Processors

Yan, C. C., Zhang, Y., Xu, J., Dai, F., Zhang, J., Dai, Q., & Wu, F. (2014) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2014.2335852 · Cited by 371 · see citing works

Blind Image Quality Assessment via Deep Learning

Hou, W., Gao, X., Tao, D., & Li, X. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2336852 · Cited by 370 · see citing works

Machine Learning in Medical Imaging

Wernick, M. N., Yang, Y., Brankov, J. G., Yourganov, G., & Strother, S. C. (2010) · IEEE Signal Processing Magazine · doi:10.1109/msp.2010.936730 · Cited by 356 · see citing works

MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network

Zhang, Z., Xie, Y., Xing, F., McGough, M., & Yang, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.378 · Cited by 354 · see citing works

An Automatic Learning-Based Framework for Robust Nucleus Segmentation

Xing, F., Xie, Y., & Yang, L. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2481436 · Cited by 353 · see citing works

Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features

Wang, H., Cruz-Roa, Á., Basavanhally, A. N., Gilmore, H. L., Shih, N. N. C., Feldman, M., Tomaszewski, J., Gonzalez, F. A., & Madabhushi, A. (2014) · Journal of medical imaging · doi:10.1117/1.jmi.1.3.034003 · Cited by 349 · see citing works

Deep learning for classification of colorectal polyps on whole-slide images

Korbar, B., Olofson, A. M., Miraflor, A. P., Nicka, C. M., Suriawinata, M. A., Torresani, L., Suriawinata, A. A., & Hassanpour, S. (2017) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_34_17 · Cited by 349 · see citing works

A Highly Parallel Framework for HEVC Coding Unit Partitioning Tree Decision on Many-core Processors

Yan, C. C., Zhang, Y., Xu, J., Dai, F., Li, L., Dai, Q., & Wu, F. (2014) · IEEE Signal Processing Letters · doi:10.1109/lsp.2014.2310494 · Cited by 348 · see citing works

Crowdsourcing the creation of image segmentation algorithms for connectomics

Arganda‐Carreras, I., Turaga, S. C., Berger, D. R., Ciresan, D. C., Giusti, A., Gambardella, L. M., Schmidhuber, J., Laptev, D., Dwivedi, S., Buhmann, J. M., et al. (2015) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2015.00142 · Cited by 336 · see citing works

Visual Turing test for computer vision systems

Geman, D., Geman, S., Hallonquist, N., & Laurent Younes, B. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1422953112 · Cited by 323 · see citing works

Accelerating Deep Convolutional Neural Networks Using Specialized Hardware

Ovtcharov, K., Ruwase, O., Kim, J.-Y., Fowers, J., Strauß, K., & Chung, E. S. (2015) · Cited by 310 · see citing works

Multi-dimensional Recurrent Neural Networks

Graves, A., Fernández, S., & Schmidhuber, J. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-74690-4_56 · Cited by 303 · see citing works

Mitosis detection in breast cancer histological images An ICPR 2012 contest

Roux, L., Racoceanu, D., Loménie, N., Kulikova, M. S., Irshad, H., Jacques, K., Capron, F., Genestie, C., Naour, G. L., & Gurcan Metin, N. (2013) · Journal of Pathology Informatics · doi:10.4103/2153-3539.112693 · Cited by 303 · see citing works

Machine learning in cell biology – teaching computers to recognize phenotypes

Sommer, C., & Gerlich, D. W. (2013) · Journal of Cell Science · doi:10.1242/jcs.123604 · Cited by 298 · see citing works

Stain Specific Standardization of Whole-Slide Histopathological Images

Bejnordi, B. E., Litjens, G. J. S., Timofeeva, N., Otte‐Höller, I., Homeyer, A., Karssemeijer, N., & van der Laak, J. A. W. M. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2476509 · Cited by 291 · see citing works

WSISA: Making Survival Prediction from Whole Slide Histopathological Images

Zhu, X.-L., Yao, J., Zhu, F.-Y., & Huang, J.-Z. (2017) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2017.725 · Cited by 286 · see citing works

Accurate Segmentation of Cervical Cytoplasm and Nuclei Based on Multiscale Convolutional Network and Graph Partitioning

Song, Y., Zhang, L., Chen, S., Ni, D., Lei, B., & Wang, T. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2430895 · Cited by 271 · see citing works

The Genotype-Tissue Expression (GTEx) project

Lonsdale, J., Thomas, J., Salvatore, M., Phillips, R., Lo, E., Shad, S., Hasz, R., Walters, G., Garcia, F., Young, N., et al. (2013) · Nature Genetics · doi:10.1038/ng.2653 · Cited by 263 · see citing works

Toward automatic phenotyping of developing embryos from videos

Ning, F., Delhomme, D., LeCun, Y., Piano, F., Bottou, L., & Barbano, P. E. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.852470 · Cited by 260 · see citing works

Pathology imaging informatics for quantitative analysis of whole-slide images

Kothari, S., Phan, J. H., Stokes, T. H., & Wang, M. D. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001540 · Cited by 259 · see citing works

Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation

Stollenga, M. F., Byeon, W., Liwicki, M., & Schmidhuber, J. (2015) · arXiv · doi:10.48550/arxiv.1506.07452 · Cited by 244 · see citing works

Cost-Sensitive Learning with Neural Networks.

Kukar, M., & Kononenko, I. (1998) · Cited by 244 · see citing works

Novel Cost-Sensitive Approach to Improve the Multilayer Perceptron Performance on Imbalanced Data

de Castro, C. L., & Braga, A. P. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2246188 · Cited by 236 · see citing works

Hardware accelerated convolutional neural networks for synthetic vision systems

Farabet, C., Martini, B., Akselrod, P., Talay, S., LeCun, Y., & Culurciello, E. (2010) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2010.5537908 · Cited by 233 · see citing works

HEp-2 Cell Image Classification With Deep Convolutional Neural Networks

Gao, Z., Wang, L., Zhou, L., & Zhang, J. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2526603 · Cited by 220 · see citing works

Scene Recognition by Manifold Regularized Deep Learning Architecture

Yuan, Y., Mou, L., & Lu, X. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2359471 · Cited by 214 · see citing works

Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology

Janowczyk, A. R., Basavanhally, A. N., & Madabhushi, A. (2016) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2016.05.003 · Cited by 211 · see citing works

Beyond Classification: Structured Regression for Robust Cell Detection Using Convolutional Neural Network

Xie, Y., Xing, F., Kong, X., Su, H., & Yang, L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_43 · Cited by 204 · see citing works

The importance of stain normalization in colorectal tissue classification with convolutional networks

Ciompi, F., Geessink, O. G. F., Bejnordi, B. E., de Souza, G. S., Baidoshvili, A., Litjens, G. J. S., van Ginneken, B., Nagtegaal, I. D., & van der Laak, J. A. W. M. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950492 · Cited by 202 · see citing works

A Multiobjective Sparse Feature Learning Model for Deep Neural Networks

Gong, M., Liu, J., Li, H., Cai, Q., & Su, L. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2469673 · Cited by 198 · see citing works

Deep convolutional activation features for large scale Brain Tumor histopathology image classification and segmentation

Xu, Y. L., Jia, Z., Ai, Y., Zhang, F., Lai, M., & Chang, E. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178109 · Cited by 193 · see citing works

Why Deep Learning Works: A Manifold Disentanglement Perspective

Brahma, P. P., Wu, D. O., & She, Y. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2496947 · Cited by 191 · see citing works

Dynamic Sampling Approach to Training Neural Networks for Multiclass Imbalance Classification

Lin, M., Tang, K., & Yao, X. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2228231 · Cited by 166 · see citing works

EEG-Based Lapse Detection With High Temporal Resolution

Davidson, P. R., Jones, R. D., & Peiris, M. T. R. (2007) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.893452 · Cited by 158 · see citing works

Deep Learning on FPGAs: Past, Present, and FuturePreprint

Lacey, G., Taylor, G. W., & Areibi, S. M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.04283 · Cited by 157 · see citing works

Learning Deep and Wide: A Spectral Method for Learning Deep Networks

Shao, L., Wu, D., & Li, X. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2308519 · Cited by 154 · see citing works

Deep Contextual Networks for Neuronal Structure Segmentation

Chen, H., Qi, X., Cheng, J., & Heng, P. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10141 · Cited by 152 · see citing works

Feature Extraction With Deep Neural Networks by a Generalized Discriminant Analysis

Stuhlsatz, A., Lippel, J., & Zielke, T. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2183645 · Cited by 146 · see citing works

Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks

Schlegl, T., Waldstein, S. M., Vogl, W., Schmidt-Erfurth, U. M., & Langs, G. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19992-4_34 · Cited by 136 · see citing works

Deep, Narrow Sigmoid Belief Networks Are Universal Approximators

Sutskever, I., & Hinton, G. E. (2008) · Neural Computation · doi:10.1162/neco.2008.12-07-661 · Cited by 131 · see citing works

Automated Histology Analysis: Opportunities for signal processing

McCann, M. T., Ozolek, J. A., Castro, C. A., Parvin, B., & Kovačević, J. (2014) · IEEE Signal Processing Magazine · doi:10.1109/msp.2014.2346443 · Cited by 128 · see citing works

Fast model-based protein homology detection without alignment

Hochreiter, S., Heusel, M., & Obermayer, K. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm247 · Cited by 128 · see citing works

GANGLION-a fast field-programmable gate array implementation of a connectionist classifier

Cox, C. E., & Blanz, W. E. (1992) · IEEE Journal of Solid-State Circuits · doi:10.1109/4.121550 · Cited by 127 · see citing works

Parallel deblocking filter for HEVC on many‐core processor

Yan, C. C., Zhang, Y., Dai, F., et al. (2014) · Electronics Letters · doi:10.1049/el.2013.3235 · Cited by 121 · see citing works

Embedded Streaming Deep Neural Networks Accelerator With Applications

Dündar, A., Jin, J., Martini, B., & Culurciello, E. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2545298 · Cited by 120 · see citing works

Joint Video and Text Parsing for Understanding Events and Answering Queries

Tu, K., Meng, M., Lee, M. W., Choe, T. E., & Zhu, S. C. (2014) · IEEE Multimedia · doi:10.1109/mmul.2014.29 · Cited by 113 · see citing works

Four challenges in medical image analysis from an industrial perspective

Weese, J., & Lorenz, C. D. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.06.023 · Cited by 112 · see citing works

Scalable energy-efficient, low-latency implementations of trained spiking Deep Belief Networks on SpiNNaker

Stromatias, E., Neil, D., Galluppi, F., Pfeiffer, M., Liu, S., & Furber, S. B. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280625 · Cited by 106 · see citing works

Learning Deep Hierarchical Visual Feature Coding

Goh, H., Thome, N., Cord, M., & Lim, J. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2307532 · Cited by 104 · see citing works

Modeling Disease Progression via Multisource Multitask Learners: A Case Study With Alzheimer’s Disease

Nie, L., Zhang, L., Meng, L., Song, X., Chang, X., & Li, X. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2520964 · Cited by 99 · see citing works

Region segmentation in histopathological breast cancer images using deep convolutional neural network

Su, H. T., Liu, F., Xie, Y., et al. (2015) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7163815 · Cited by 98 · see citing works

Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders

Su, H., Xing, F., Kong, X., Xie, Y., Zhang, S., & Yang, L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_46 · Cited by 90 · see citing works

Efficient parallel HEVC intra‐prediction on many‐core processor

Yan, C. C., Zhang, Y., Dai, F., et al. (2014) · Electronics Letters · doi:10.1049/el.2014.0611 · Cited by 88 · see citing works

Deep Voting: A Robust Approach Toward Nucleus Localization in Microscopy Images

Xie, Y., Kong, X., Xing, F., Liu, F., Su, H., & Yang, L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_45 · Cited by 79 · see citing works

Deep and Shallow Architecture of Multilayer Neural Networks

Chang, C.-H. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2387439 · Cited by 78 · see citing works

Prostate: Registration of Digital Histopathologic Images to in Vivo MR Images Acquired by Using Endorectal Receive Coil

Ward, A. D., Crukley, C., McKenzie, C. A., Montreuil, J., Gibson, E., Romagnoli, C., Gómez, J. A., Moussa, M., Chin, J. L., Bauman, G. S., et al. (2012) · Radiology · doi:10.1148/radiol.12102294 · Cited by 75 · see citing works

TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References

Zhang, Z., Chen, P., Sapkota, M., & Yang, L. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66179-7_37 · Cited by 71 · see citing works

Highly Parallel Framework for HEVC Motion Estimation on Many-Core Platform

Yan, C. C., Zhang, Y., Dai, F., et al. (2013) · DCC · doi:10.1109/dcc.2013.14 · Cited by 70 · see citing works

Sparseness Analysis in the Pretraining of Deep Neural Networks

Li, J., Zhang, T., Luo, W., Yang, J. J., Yuan, X., & Zhang, J. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2541681 · Cited by 68 · see citing works

Spatial Clockwork Recurrent Neural Network for Muscle Perimysium Segmentation

Xie, Y., Zhang, Z., Sapkota, M., & Yang, L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46723-8_22 · Cited by 66 · see citing works

Microscopy cell counting with fully convolutional regression networks

Xie, W., Noble, J. A., & Zisserman, A. (2015) · Cited by 66 · see citing works

An FPGA-based stream processor for embedded real-time vision with Convolutional Networks

Farabet, C., Poulet, C., & LeCun, Y. (2009) · doi:10.1109/iccvw.2009.5457611 · Cited by 53 · see citing works

Deep learning for automatic cell detection in wide-field microscopy zebrafish images

Dong, B., Shao, L., Costa, M. D., Bandmann, O., & Frangi, A. F. (2015) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7163986 · Cited by 50 · see citing works

Deep models for brain EM image segmentation: novel insights and improved performance

Fakhry, A., Peng, H., & Ji, S. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw165 · Cited by 49 · see citing works

Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image InterpretationPreprint

Shin, H.-C., Lü, L., Kim, L., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.00670 · Cited by 47 · see citing works

Adaptation to New Microphones Using Artificial Neural Networks With Trainable Activation Functions

Siniscalchi, S. M., & Salerno, V. M. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2550532 · Cited by 46 · see citing works

Deep Networks are Effective Encoders of Periodicity

Szymanski, L., & McCane, B. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2296046 · Cited by 45 · see citing works

Characterization of Tissue Histopathology via Predictive Sparse Decomposition and Spatial Pyramid Matching

Chang, H., Nayak, N. M., Spellman, P. T., et al. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40763-5_12 · Cited by 44 · see citing works

A Systematic Review of the Interobserver Variability for Histology in the Differentiation between Squamous and Nonsquamous Non-small Cell Lung Cancer

Paech, D., Weston, A. R., Pavlakis, N., Gill, A. J., Rajan, N., Barraclough, H., Fitzgerald, B., & Kooten, M. V. (2010) · Journal of Thoracic Oncology · doi:10.1097/jto.0b013e3181fc0878 · Cited by 43 · see citing works

Automatic detection of cell divisions (mitosis) in live-imaging microscopy images using Convolutional Neural Networks

Shkolyar, A., Gefen, A., Benayahu, D., & Greenspan, H. K. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318469 · Cited by 42 · see citing works

A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum-Weight Independent Set

Liu, F., & Yang, L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_42 · Cited by 33 · see citing works

Cutting Out the Middleman: Measuring Nuclear Area in Histopathology Slides Without Segmentation

Veta, M., van Diest, P. J., & Pluim, J. P. W. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46723-8_73 · Cited by 32 · see citing works

Detecting 10,000 Cells in One Second

Xu, Z., & Huang, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46723-8_78 · Cited by 32 · see citing works

Iteratively training classifiers for circulating tumor cell detection

Mao, Y., Yin, Z., & Schober, J. M. (2015) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7163847 · Cited by 31 · see citing works

Resolution of Singularities Introduced by Hierarchical Structure in Deep Neural Networks

Nitta, T. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2580741 · Cited by 30 · see citing works

Stacked Predictive Sparse Coding for Classification of Distinct Regions in Tumor Histopathology

Chang, H., Zhou, Y., Spellman, P. T., & Parvin, B. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.28 · Cited by 26 · see citing works

Image Understanding Applications of Lattice Autoassociative Memories

Graña, M., & Chyzhyk, D. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2461451 · Cited by 26 · see citing works

Robust Selection-Based Sparse Shape Model for Lung Cancer Image Segmentation

Xing, F., & Yang, L. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40760-4_51 · Cited by 23 · see citing works

Predicting chemoinsensitivity in breast cancer with ’omics/digital pathology data fusion

Savage, R. S., & Yuan, Y. (2016) · Royal Society Open Science · doi:10.1098/rsos.140501 · Cited by 21 · see citing works

Sequence-specific bias correction for RNA-seq data using recurrent neural networks

Zhang, Y., Yamaguchi, R., Imoto, S., & Miyano, S. (2017) · BMC Genomics · doi:10.1186/s12864-016-3262-5 · Cited by 21 · see citing works

Neutrophils Identification by Deep Learning and Voronoi Diagram of Clusters

Wang, J., MacKenzie, J. D., Ramachandran, R., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_27 · Cited by 16 · see citing works

Watershed merge forest classification for electron microscopy image stack segmentation

Liu, T., Seyedhosseini, M., Ellisman, M. H., et al. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738838 · Cited by 14 · see citing works

Transfer Shape Modeling Towards High-Throughput Microscopy Image Segmentation

Xing, F., Shi, X., Zhang, Z., Cai, J., Xie, Y., & Yang, L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46726-9_22 · Cited by 14 · see citing works

Back-Propagation Algorithm Achieving 5 Gops on the Virtex-E

Paul, K., & Rajopadhye, S. V. (2006) · Springer eBooks · doi:10.1007/0-387-28487-7_5 · Cited by 13 · see citing works

On the Impact of Approximate Computation in an Analog DeSTIN Architecture

Young, S. R., Lu, J., Holleman, J. H., et al. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2283730 · Cited by 13 · see citing works

Fast Cell Segmentation Using Scalable Sparse Manifold Learning and Affine Transform-Approximated Active Contour

Xing, F., & Yang, L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_40 · Cited by 10 · see citing works

Context Dependent Encoding Using Convolutional Dynamic Networks

Chalasani, R., & Príncipe, J. C. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2360060 · Cited by 9 · see citing works

Scale normalization of histopathological images for batch invariant cancer diagnostic models

Kothari, S., Phan, J. H., & Wang, M. D. (2012) · doi:10.1109/embc.2012.6346943 · Cited by 7 · see citing works

Weakly-Supervised Structured Output Learning with Flexible and Latent Graphs Using High-Order Loss Functions

Carneiro, G., Peng, T., Bayer, C., et al. (2015) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.81 · Cited by 7 · see citing works

Automatic muscle perimysium annotation using deep convolutional neural network

Sapkota, M., Xing, F., Su, H., & Yang, L. (2015) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7163850 · Cited by 5 · see citing works

Restoration of distorted colour microscopic images from transverse chromatic aberration of imperfect lenses

WU, H.-S., Murray, J., Morgello, S., et al. (2010) · Journal of Microscopy · doi:10.1111/j.1365-2818.2010.03443.x · Cited by 3 · see citing works

Deep learning for cellular image analysis

Moen, E., Bannon, D., Kudo, T., Graf, W., Covert, M., & Van Valen, D. (2019) · Nature Methods · doi:10.1038/s41592-019-0403-1 · Cited by 1,225 · see citing works

Deep Learning in Medical Ultrasound Analysis: A Review

Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S. X., Ni, D., & Wang, T. (2019) · Engineering · doi:10.1016/j.eng.2018.11.020 · Cited by 798 · see citing works

Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images

Zhou, K., Xiao, Y., Yang, J., Cheng, J., Liu, W., Luo, W., Gu, Z., Liu, J., & Gao, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58565-5_22 · Cited by 146 · see citing works

A bird's-eye view of deep learning in bioimage analysis

Meijering, E. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.08.003 · Cited by 126 · see citing works

Automated segmentation of insect anatomy from micro‐CT images using deep learning

Toulkeridou, E., Gutierrez, C. E., Baum, D., Doya, K., & Economo, E. P. (2023) · Natural Sciences · doi:10.1002/ntls.20230010 · Cited by 13 · see citing works

An Adaptable Real-Time Object Detection for Traffic Surveillance using R-CNN over CNN with Improved Accuracy

Vinod, G., & Padmapriya, G. (2022) · International Conference on Business Analytics for Technology and Security (ICBATS) · doi:10.1109/icbats54253.2022.9759030 · Cited by 11 · see citing works

Deep fake Detection Through Deep Learning

Doke, Y. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.51630 · Cited by 5 · see citing works

Why & When Deep Learning Works: Looking Inside Deep LearningsPreprint

Ronen, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.03921 · Cited by 1 · see citing works

SEGMENTATION OF MEDICAL IMAGES BY CONVOLUTIONAL NEURAL NETWORKS

Lutsenko, V. S., & Shukhman, A. E. (2022) · Vestnik komp iuternykh i informatsionnykh tekhnologii · doi:10.14489/vkit.2022.06.pp.040-050 · Cited by 1 · see citing works

Implementing convolutional neural network model for prediction in medical imaging

Pandey, R., Sahai, A., & Kashyap, H. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-824054-0.00024-1 · Cited by 8 · see citing works