Deep learning for imaging and detection of microorganisms

A Deep Learning Approach to Antibiotic Discovery

Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.021 · Cited by 2,463 · see citing works

A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis

Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., Mahendiran, T., Moraes, G., Shamdas, M., Kern, C., et al. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30123-2 · Cited by 1,883 · see citing works

Deep learning enables cross-modality super-resolution in fluorescence microscopy

Wang, H., Rivenson, Y., Jin, Y., Wei, Z., Gao, R., Günaydın, H., Bentolila, L. A., Kural, C., & Özcan, A. (2018) · Nature Methods · doi:10.1038/s41592-018-0239-0 · Cited by 788 · see citing works

Deep Learning and Its Applications in Biomedicine

Cao, C., Liu, F., Tan, H., Song, D., Shu, W., Li, W., Zhou, Y., Bo, X., & Xie, Z. (2018) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2017.07.003 · Cited by 625 · see citing works

Modern Uses of Electron Microscopy for Detection of Viruses

Goldsmith, C. S., & Miller, S. (2009) · Clinical Microbiology Reviews · doi:10.1128/cmr.00027-09 · Cited by 336 · see citing works

DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning

Lugagne, J., Lin, H., & Dunlop, M. J. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007673 · Cited by 245 · see citing works

Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning

Wu, Y., Rivenson, Y., Wang, H., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0622-5 · Cited by 242 · see citing works

Deep Learning with Microfluidics for Biotechnology

Riordon, J., Sovilj, D., Sanner, S., Sinton, D. A., & Young, E. W. K. (2018) · Trends in biotechnology · doi:10.1016/j.tibtech.2018.08.005 · Cited by 225 · see citing works

Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning

Wang, H., Koydemir, H. C., Qiu, Y., Bai, B., Zhang, Y., Jin, Y., Tok, S., Yılmaz, E. C., Gumustekin, E., Rivenson, Y., et al. (2020) · Light Science & Applications · doi:10.1038/s41377-020-00358-9 · Cited by 214 · see citing works

Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis

Levine, A., Schlosser, C. L., Grewal, J., Coope, R., Jones, S. J. M., & Yip, S. S. F. (2019) · Trends in cancer · doi:10.1016/j.trecan.2019.02.002 · Cited by 202 · see citing works

Deep learning approach to bacterial colony classification

Zieliński, B., Plichta, A., Misztal, K., Spurek, P., Brzychczy‐Włoch, M., & Ochońska, D. (2017) · PLoS ONE · doi:10.1371/journal.pone.0184554 · Cited by 194 · see citing works

Automated Interpretation of Blood Culture Gram Stains by Use of a Deep Convolutional Neural Network

Smith, K. P., Kang, A. D., & Kirby, J. E. (2017) · Journal of Clinical Microbiology · doi:10.1128/jcm.01521-17 · Cited by 194 · see citing works

Bacterial colony counting with Convolutional Neural Networks in Digital Microbiology Imaging

Ferrari, A., Lombardi, S., & Signoroni, A. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.07.016 · Cited by 174 · see citing works

Deep learning approach to describe and classify fungi microscopic images

Zieliński, B., Sroka-Oleksiak, A., Rymarczyk, D., Piekarczyk, A., & Brzychczy-Włoch, M. (2020) · PLOS ONE · doi:10.1371/journal.pone.0234806 · Cited by 126 · see citing works

A fungus spores dataset and a convolutional neural network based approach for fungus detection

Tahir, M. W., Zaidi, N. A., Rao, A. A., Blank, R., Vellekoop, M. J., & Lang, W. (2018) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2018.2839585 · Cited by 88 · see citing works

Computational microscopic imaging for malaria parasite detection: a systematic review

Das, D. K., Mukherjee, R., & Chakraborty, C. (2015) · Journal of Microscopy · doi:10.1111/jmi.12270 · Cited by 87 · see citing works

Three-dimensional visualization and a deep-learning model reveal complex fungal parasite networks in behaviorally manipulated ants

Fredericksen, M., Zhang, Y., Hazen, M. L., Loreto, R. G., Mangold, C. A., Chen, D. Z., & Hughes, D. P. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1711673114 · Cited by 86 · see citing works

Virus detection using nanoparticles and deep neural network–enabled smartphone system

Draz, M. S., Vasan, A., Muthupandian, A., Kanakasabapathy, M. K., Thirumalaraju, P., Sreeram, A., Krishnakumar, S., Yogesh, V., Lin, W., Yu, X. G., et al. (2020) · Science Advances · doi:10.1126/sciadv.abd5354 · Cited by 81 · see citing works

Performance of convolutional neural networks for identification of bacteria in 3D microscopy datasets

Hay, E. A., & Parthasarathy, R. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006628 · Cited by 71 · see citing works

Segmentation of virus particle candidates in transmission electron microscopy images

Kylberg, G., Uppström, M., Hedlund, K. O., et al. (2011) · Journal of Microscopy · doi:10.1111/j.1365-2818.2011.03556.x · Cited by 53 · see citing works

Geometry-Aware Cell Detection with Deep Learning

Jiang, H., Li, S., Liu, W., Zheng, H., Liu, J., & Zhang, Y. (2020) · mSystems · doi:10.1128/msystems.00840-19 · Cited by 51 · see citing works

Virus Particle Detection by Convolutional Neural Network in Transmission Electron Microscopy Images

Ito, E., Sato, T., Sano, D., UTAGAWA, E., & Kato, T. (2018) · Food and Environmental Virology · doi:10.1007/s12560-018-9335-7 · Cited by 38 · see citing works

Electron Cryotomography of Bacterial Secretion Systems

Oikonomou, C. M., & Jensen, G. J. (2019) · Microbiology Spectrum · doi:10.1128/microbiolspec.psib-0019-2018 · Cited by 36 · see citing works

Detection of herpesvirus capsids in transmission electron microscopy images using transfer learning

Devan, K. S., Walther, P., von Einem, J., Ropinski, T., Kestler, H. A., & Read, C. (2018) · Histochemistry and Cell Biology · doi:10.1007/s00418-018-1759-5 · Cited by 33 · see citing works

Parasitologist-level classification of apicomplexan parasites and host cell with deep cycle transfer learning (DCTL)

Li, S., Yang, Q., Jiang, H., Cortés-Vecino, J., Zhang, Y., & Valencia, A. (2020) · Bioinform. · doi:10.1093/bioinformatics/btaa513 · Cited by 33 · see citing works

Reducing the U-Net size for practical scenarios: Virus recognition in electron microscopy images

Matuszewski, D. J., & Sintorn, I. (2019) · Comput. Methods Programs Biomed. · doi:10.1016/j.cmpb.2019.05.026 · Cited by 30 · see citing works

Transfer Learning for Toxoplasma gondii Recognition

Li, S., Li, A., Lara, D. A. M., Gómez‐Marín, J. E., Juhas, M., & Zhang, Y. (2020) · mSystems · doi:10.1128/msystems.00445-19 · Cited by 26 · see citing works

Improved automatic detection of herpesvirus secondary envelopment stages in electron microscopy by augmenting training data with synthetic labelled images generated by a generative adversarial network

Devan, K. S., Walther, P., von Einem, J., et al. (2020) · Cellular Microbiology · doi:10.1111/cmi.13280 · Cited by 18 · see citing works

Virus identification in electron microscopy images by residual mixed attention network

Xiao, C., Chen, X., Xie, Q., Li, G., Xiao, H., Song, J., & Han, H. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105766 · Cited by 17 · see citing works

Bildverarbeitung für die Medizin 2019

(2019) · Informatik aktuell · doi:10.1007/978-3-658-25326-4 · Cited by 13 · see citing works

Cellular Imaging of Intracellular Bacterial Pathogens

Stévenin, V., & Enninga, J. (2019) · Microbiology Spectrum · doi:10.1128/microbiolspec.bai-0017-2019 · Cited by 5 · see citing works

Deep Segmentation of Bacteria at Different Stages of the Life Cycle

Spilger, R., Schwackenhofer, T., Kaspar, C., et al. (2020) · Informatik aktuell · doi:10.1007/978-3-658-29267-6_2 · Cited by 1 · see citing works

A review of Earth Artificial Intelligence

Sun, Z., Sandoval, L., Crystal-Ornelas, R., Mousavi, S. M., Wang, J., Lin, C., Cristea, N., Tong, D., Carande, W. H., Ma, X., et al. (2022) · Computers & Geosciences · doi:10.1016/j.cageo.2022.105034 · Cited by 271 · see citing works

Artificial intelligence-based tools applied to pathological diagnosis of microbiological diseases

Marletta, S., L’Imperio, V., Eccher, A., Antonini, P., Santonicco, N., Girolami, I., Dei Tos, A. P., Sbaraglia, M., Pagni, F., Brunelli, M., et al. (2023) · Pathology - Research and Practice · doi:10.1016/j.prp.2023.154362 · Cited by 49 · see citing works

A decision support system for the detection of cutaneous fungal infections using artificial intelligence

Rappoport, N., Goldinger, G., Debby, A., Molchanov, Y., Barak, Y., Gildenblat, J., Hadar, O., Sagiv, C., & Barzilai, A. (2024) · Pathology - Research and Practice · doi:10.1016/j.prp.2024.155480 · Cited by 11 · see citing works

Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives

Della Mura, M., Sorino, J., Colagrande, A., Daruish, M., Ingravallo, G., Massaro, A., Cazzato, G., Lupo, C., Casatta, N., Ribatti, D., et al. (2025) · Medical Sciences · doi:10.3390/medsci13020070 · Cited by 6 · see citing works

Overview of deep learning in medical imaging

Suzuki, K. (2017) · Radiological Physics and Technology · doi:10.1007/s12194-017-0406-5 · Cited by 1,102 · see citing works

Survey of Machine Learning Applications of Convolutional Neural Networks to Medical Image Analysis

Kumar, K. N. (2021) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2021.38947 · Cited by 2 · see citing works

Exploring Deep Learning for View-Based 3D Model Retrieval

Gao, Z., Li, Y., & Wan, S. (2020) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3377876 · Cited by 108 · see citing works

Deep learning ensemble 2D CNN approach towards the detection of lung cancer

Shah, A. A., Malik, H. A. M., Muhammad, A., Alourani, A., & Butt, Z. A. (2023) · Scientific Reports · doi:10.1038/s41598-023-29656-z · Cited by 211 · see citing works

A Comprehensive Analysis of Machine Learning Techniques in Biomedical Image Processing Using Convolutional Neural Network

Shrivastava, A., Chakkaravathy, M., & Shah, M. A. (2022) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i56241.2022.10072911 · Cited by 27 · see citing works