Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks

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

Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position

Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759 · 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

Neural Networks and the Bias/Variance Dilemma

Geman, S., Bienenstock, E., & Doursat, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.1 · Cited by 3,578 · see citing works

Hierarchical models of object recognition in cortex

Riesenhuber, M., & Poggio, T. (1999) · Nature Neuroscience · doi:10.1038/14819 · Cited by 3,309 · see citing works

Best practices for convolutional neural networks applied to visual document analysis

Simard, P., Steinkraus, D., & Platt, J. C. (2005) · Proceedings of the International Conference on Document Analysis and Recognition · doi:10.1109/icdar.2003.1227801 · Cited by 2,805 · see citing works

Learning Hierarchical Features for Scene Labeling

Farabet, C., Couprie, C., Najman, L., & LeCun, Y. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.231 · Cited by 2,620 · see citing works

Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition

Scherer, D., Müller, A., & Behnke, S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15825-4_10 · Cited by 1,556 · see citing works

Untitled work

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

Untitled work

(2011) · doi:10.5591/978-1-57735-516-8/ijcai11-210 · Cited by 1,229 · 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

Hierarchical Neural Networks for Image Interpretation

Behnke, S. (2003) · Lecture notes in computer science · doi:10.1007/b11963 · Cited by 225 · see citing works

Classification of mitotic figures with convolutional neural networks and seeded blob features

Malon, C. D., & Cosatto, E. (2013) · Journal of Pathology Informatics · doi:10.4103/2153-3539.112694 · Cited by 193 · see citing works

Automated mitosis detection in histopathology using morphological and multi-channel statistics features

Irshad, H. (2013) · Journal of Pathology Informatics · doi:10.4103/2153-3539.112695 · Cited by 148 · see citing works

Synaptic Rewiring for Topographic Map Formation

Bamford, S. A., Murray, A. F., & Willshaw, D. J. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87559-8_23 · Cited by 145 · see citing works

A gamma-gaussian mixture model for detection of mitotic cells in breast cancer histopathology images

Khan, A. M., Eldaly, H., & Rajpoot, N. (2013) · Journal of Pathology Informatics · doi:10.4103/2153-3539.112696 · Cited by 109 · see citing works

Mitosis detection using generic features and an ensemble of cascade adaboosts

Faik Boray, T. E. K. (2013) · Journal of Pathology Informatics · doi:10.4103/2153-3539.112697 · Cited by 63 · see citing works

Detecting mitotic figures in breast cancer histopathology images

Veta, M., van Diest, P. J., & Pluim, J. P. W. (2013) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2006626 · Cited by 52 · see citing works

Automated Mitosis Detection based on eXclusive Independent Component Analysis

Huang, Z., & Lee, H.-K. (2013) · Cited by 41 · see citing works

Heterogeneous Conditional Random Field: Realizing joint detection and segmentation of cell regions in microscopic images

Pan, J., Kanade, T., & Chen, M. (2010) · 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.2010.5540037 · Cited by 24 · 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

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 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

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906 · 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

A guide to deep learning in healthcare

Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312 · see citing works

Deep learning in medical image analysis

Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968 · see citing works

Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.004 · Cited by 3,498 · 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

Region-based convolutional networks for accurate object detection and segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2437384 · Cited by 2,912 · see citing works

Artificial intelligence in healthcare

Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817 · see citing works

Opportunities and obstacles for deep learning in biology and medicine

Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310 · see citing works

The rise of deep learning in drug discovery

Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.039 · Cited by 1,770 · see citing works

Deep learning-enabled medical computer vision

Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J., & Socher, R. (2021) · npj Digital Medicine · doi:10.1038/s41746-020-00376-2 · Cited by 1,406 · 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

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

Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images

Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.086 · Cited by 1,027 · see citing works

DeepTox: toxicity prediction using deep learning

Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016) · Frontiers in Environmental Science · doi:10.3389/fenvs.2015.00080 · Cited by 1,081 · see citing works

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

Litjens, G., Sánchez, C. I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., Hulsbergen - van de Kaa, C., Bult, P., van Ginneken, B., & van der Laak, J. (2016) · Scientific Reports · doi:10.1038/srep26286 · Cited by 1,069 · see citing works

Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis

Suk, H.-I., Lee, S.-W., & Shen, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.077 · Cited by 968 · see citing works

Large scale deep learning for computer aided detection of mammographic lesions

Kooi, T., Litjens, G., van Ginneken, B., Gubern-Mérida, A., Sánchez, C. I., Mann, R., den Heeten, A., & Karssemeijer, N. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.07.007 · Cited by 965 · see citing works

Deep learning in histopathology: the path to the clinic

van der Laak, J., Litjens, G., & Ciompi, F. (2021) · Nature Medicine · doi:10.1038/s41591-021-01343-4 · Cited by 902 · see citing works

Deep convolutional neural networks for multi-modality isointense infant brain image segmentation

Zhang, W., Li, R., Deng, H., Wang, L., Lin, W., Ji, S., & Shen, D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.061 · Cited by 817 · see citing works

BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment

Kawahara, J., Brown, C. J., Miller, S. P., Booth, B. G., Chau, V., Grunau, R. E., Zwicker, J. G., & Hamarneh, G. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.09.046 · Cited by 813 · see citing works

Deep neural network models for computational histopathology: A survey

Srinidhi, C. L., Ciga, O., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101813 · Cited by 749 · see citing works

Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success

Thrall, J. H., Li, X., Li, Q., Cruz, C., Do, S., Dreyer, K., & Brink, J. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.12.026 · Cited by 710 · see citing works

Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance.

Yuan, Y., Chao, M., & Lo, Y.-C. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2695227 · Cited by 687 · see citing works

Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

Dou, Q., Chen, H., Yu, L., Zhao, L., Qin, J., Wang, D., Mok, V. C., Shi, L., & Heng, P.-A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528129 · Cited by 679 · see citing works

Deep learning based tissue analysis predicts outcome in colorectal cancer

Bychkov, D., Linder, N., Turkki, R., Nordling, S., Kovanen, P. E., Verrill, C., Walliander, M., Lundin, M., Haglund, C., & Lundin, J. (2018) · Scientific Reports · doi:10.1038/s41598-018-21758-3 · Cited by 644 · 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

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

Van Valen, D. A., Kudo, T., Lane, K. M., Macklin, D. N., Quach, N. T., DeFelice, M. M., Maayan, I., Tanouchi, Y., Ashley, E. A., & Covert, M. W. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005177 · Cited by 557 · see citing works

Understanding adversarial attacks on deep learning based medical image analysis systems

Ma, X., Niu, Y., Gu, L., Wang, Y., Zhao, Y., Bailey, J., & Lu, F. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2020.107332 · Cited by 553 · see citing works

Improving Computer-Aided Detection UsingConvolutional Neural Networks and Random View Aggregation

Roth, H. R., Lu, L., Liu, J., Yao, J., Seff, A., Cherry, K., Kim, L., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2482920 · Cited by 608 · see citing works

Leveraging uncertainty information from deep neural networks for disease detection

Leibig, C., Allken, V., Ayhan, M. S., Berens, P., & Wahl, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-17876-z · Cited by 510 · see citing works

Deep learning in microscopy image analysis: a survey

Xing, F., Xie, Y., Su, H., Liu, F., & Yang, L. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2766168 · Cited by 460 · 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

Chest pathology detection using deep learning with non-medical training

Bar, Y., Diamant, I., Wolf, L., Lieberman, S., Konen, E., & Greenspan, H. (2015) · 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7163871 · Cited by 392 · see citing works

Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks

Chen, H., Ni, D., Qin, J., Li, S., Yang, X., Wang, T., & Heng, P. A. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2425041 · Cited by 379 · 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

Deep learning in drug discovery: opportunities, challenges and future prospects

Lavecchia, A. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.07.006 · Cited by 353 · see citing works

The present and future of deep learning in radiology

Saba, L., Biswas, M., Kuppili, V., Cuadrado Godia, E., Suri, H. S., Edla, D. R., Omerzu, T., Laird, J. R., Khanna, N. N., Mavrogeni, S., et al. (2019) · European Journal of Radiology · doi:10.1016/j.ejrad.2019.02.038 · Cited by 353 · see citing works

Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype

Couture, H. D., Williams, L. A., Geradts, J., Nyante, S. J., Butler, E. N., Marron, J. S., Perou, C. M., Troester, M. A., & Niethammer, M. (2018) · npj Breast Cancer · doi:10.1038/s41523-018-0079-1 · Cited by 327 · see citing works

Compute Trends Across Three Eras of Machine Learning

Sevilla, J., Heim, L., Ho, A., Besiroglu, T., Hobbhahn, M., & Villalobos, P. (2022) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9891914 · Cited by 363 · see citing works

Deep neural networks for anatomical brain segmentation

de Brebisson, A., & Montana, G. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301312 · Cited by 330 · see citing works

Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks

Tellez, D., Balkenhol, M., Otte-Höller, I., van de Loo, R., Vogels, R., Bult, P., Wauters, C., Vreuls, W., Mol, S., Karssemeijer, N., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2820199 · Cited by 288 · see citing works

Translational AI and deep learning in diagnostic pathology

Serag, A., Ion-Margineanu, A., Qureshi, H., McMillan, R., Saint Martin, M.-J., Diamond, J., O'Reilly, P., & Hamilton, P. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00185 · Cited by 274 · see citing works

Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews

Khosravi, M., Zare, Z., Mojtabaeian, S. M., & Izadi, R. (2024) · Health Services Research and Managerial Epidemiology · doi:10.1177/23333928241234863 · Cited by 265 · see citing works

Content-based brain tumor retrieval for MR images using transfer learning

Swati, Z. N. K., Zhao, Q., Kabir, M., Ali, F., Ali, Z., Ahmed, S., & Lu, J. (2019) · IEEE Access · doi:10.1109/access.2019.2892455 · Cited by 246 · see citing works

Deep learning for small and big data in psychiatry

Koppe, G., Meyer-Lindenberg, A., & Durstewitz, D. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-020-0767-z · Cited by 189 · see citing works

Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks

Ma, J., Wu, F., Jiang, T., Zhao, Q., & Kong, D. (2017) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-017-1649-7 · Cited by 187 · see citing works

Anatomy-specific classification of medical images using deep convolutional nets

Roth, H. R., Lee, C. T., Shin, H.-C., Seff, A., Kim, L., Yao, J., Lu, L., & Summers, R. M. (2015) · 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7163826 · Cited by 188 · see citing works

Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study

Maier, O., Schröder, C., Forkert, N. D., Martinetz, T., & Handels, H. (2015) · PLOS ONE · doi:10.1371/journal.pone.0145118 · Cited by 150 · see citing works

Marginal space deep learning: efficient architecture for volumetric image parsing

Ghesu, F. C., Krubasik, E., Georgescu, B., Singh, V., Zheng, Y., Hornegger, J., & Comaniciu, D. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2538802 · Cited by 142 · see citing works

Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

Alzubaidi, L., Al-Shamma, O., Fadhel, M. A., Farhan, L., Zhang, J., & Duan, Y. (2020) · Electronics · doi:10.3390/electronics9030445 · Cited by 143 · see citing works

Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images

Ma, J., Wu, F., Jiang, T., Zhu, J., & Kong, D. (2017) · Medical Physics · doi:10.1002/mp.12134 · Cited by 142 · see citing works

Deep learning in biomedical data science

Baldi, P. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013343 · Cited by 119 · see citing works

A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images

Wang, Y., Qiu, Y., Thai, T., Moore, K., Liu, H., & Zheng, B. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.03.017 · Cited by 110 · see citing works

Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays

Shi, Y., Nguyen, L., Oh, S., Liu, X., Koushan, F., Jameson, J. R., & Kuzum, D. (2018) · Nature Communications · doi:10.1038/s41467-018-07682-0 · Cited by 106 · see citing works

SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging

Duggal, R., Gupta, A., Gupta, R., & Mallick, P. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66179-7_50 · Cited by 99 · see citing works

Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articles

Gamper, J., & Rajpoot, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01628 · Cited by 75 · see citing works

Deep Feature Learning with Discrimination Mechanism for Brain Tumor Segmentation and Diagnosis

Zhao, L., & Jia, K. (2015) · 2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP) · doi:10.1109/iih-msp.2015.41 · Cited by 67 · see citing works

AI support for ethical decision-making around resuscitation: proceed with care

Biller-Andorno, N., Ferrario, A., Joebges, S., Krones, T., Massini, F., Barth, P., Arampatzis, G., & Krauthammer, M. (2022) · Journal of Medical Ethics · doi:10.1136/medethics-2020-106786 · Cited by 67 · see citing works

Artificial Intelligence-aided diagnosis of breast cancer lymph node metastasis on histologic slides in a digital workflow

Challa, B., Tahir, M., Hu, Y., Kellough, D., Lujan, G., Sun, S., Parwani, A. V., & Li, Z. (2023) · Modern Pathology · doi:10.1016/j.modpat.2023.100216 · Cited by 61 · see citing works

Lessons Learned from Designing an AI-Enabled Diagnosis Tool for Pathologists

Gu, H., Huang, J., Hung, L., & Chen, X. '. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449084 · Cited by 56 · see citing works

A Deep Learning Approach for Histopathological Diagnosis of Onychomycosis: Not Inferior to Analogue Diagnosis by Histopathologists

Decroos, F., Springenberg, S., Lang, T., Päpper, M., Zapf, A., Metze, D., Steinkraus, V, & Böer-Auer, A. (2021) · Acta Dermato Venereologica · doi:10.2340/00015555-3893 · Cited by 51 · see citing works

Attention-Guided Multi-Branch Convolutional Neural Network for Mitosis Detection From Histopathological Images

Lei, H., Liu, S., Elazab, A., Gong, X., & Lei, B. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3027566 · Cited by 46 · see citing works

AI support for ethical decision-making around resuscitation: proceed with care

Biller-Andorno, N., Ferrario, A., Joebges, S., Krones, T., Massini, F., Barth, P., Arampatzis, G., & Krauthammer, M. (2020) · doi:10.1101/2020.08.17.20171769 · Cited by 67 · see citing works

Dropout-Enabled Ensemble Learning for Multi-scale Biomedical Data

Momeni, A., Thibault, M., & Gevaert, O. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-11723-8_41 · Cited by 12 · 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

Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge

Gu, H., Haeri, M., Ni, S., Williams, C. K., Zarrin-Khameh, N., Magaki, S., & Chen, X. ‘. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-33658-4_21 · Cited by 6 · see citing works

A Fully Trainable Network with RNN-based PoolingPreprint

Li, S., Li, W., Cook, C., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.05157 · Cited by 1 · see citing works

A fully trainable network with RNN-based pooling

Li, S., Li, W., Cook, C., Zhu, C., & Gao, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.02.004 · Cited by 24 · see citing works

Alpha-Pooling for Convolutional Neural Networks.Preprint

Eom, H., & Choi, H. (2018) · arXiv (Cornell University) · Cited by 2 · see citing works

Multiactivation Pooling Method in Convolutional Neural Networks for Image Recognition

Zhao, Q., Lyu, S., Zhang, B., & Feng, W. (2018) · Wireless Communications and Mobile Computing · doi:10.1155/2018/8196906 · Cited by 29 · 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