A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded

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

Unpaired image-to-image translation using cycle-consistent adversarial networks

Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602 · see citing works

Image-to-Image Translation with Conditional Adversarial Networks

Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930 · see citing works

Statistical Methods for Rates and Proportions

Fleiss, J. L., Levin, B., & Paik, M. C. (2003) · Wiley Series in Probability and Statistics · doi:10.1002/0471445428 · Cited by 18,646 · see citing works

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

(2017) · doi:10.1109/cvpr35066.2017 · Cited by 7,281 · see citing works

International Conference on Learning Representations (ICLR 2013)

松尾, 豊. (2013) · 人工知能学会誌 = Journal of Japanese Society for Artificial Intelligence · Cited by 6,286 · see citing works

“Least squares generative adversarial networks,”

Mao, X., Li, Q., Xie, H., Lau, R. Y. K., Wang, Z., & Smolley, S. P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.304 · Cited by 5,437 · see citing works

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

(2018) · doi:10.1109/cvpr40276.2018 · Cited by 4,769 · see citing works

Generative Adversarial Networks: An Overview

Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2765202 · Cited by 4,540 · see citing works

Unsupervised Feature Learning via Non-parametric Instance Discrimination

Wu, Z., Xiong, Y., Yu, S. X., & Lin, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00393 · Cited by 3,664 · see citing works

2017 IEEE International Conference on Computer Vision (ICCV)

(2017) · doi:10.1109/iccv37128.2017 · Cited by 3,129 · see citing works

Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930 · see citing works

Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., & Fuchs, T. J. (2019) · Nature Medicine · doi:10.1038/s41591-019-0508-1 · Cited by 2,799 · see citing works

Multimodal Unsupervised Image-to-Image Translation

Huang, X., Liu, M.-Y., Belongie, S., & Kautz, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_11 · Cited by 2,597 · see citing works

Data-efficient and weakly supervised computational pathology on whole-slide images

Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Chen, R. J., Barbieri, M., & Mahmood, F. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-020-00682-w · Cited by 2,282 · see citing works

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

(2020) · doi:10.1109/cvpr42600.2020 · Cited by 1,805 · see citing works

Contrastive Learning for Unpaired Image-to-Image Translation

Park, T., Efros, A. A., Zhang, R., & Zhu, J.-Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58545-7_19 · Cited by 1,605 · see citing works

Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning

Rivenson, Y., Wang, H., Wei, Z., Haan, K. D., Zhang, Y., Wu, Y., Günaydın, H., Zuckerman, J. E., Chong, T., Sisk, A. E. J., et al. (2019) · Nature Biomedical Engineering · doi:10.1038/s41551-019-0362-y · Cited by 653 · see citing works

An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis

Chen, P.-H. C., Gadepalli, K., MacDonald, R., Liu, Y., Kadowaki, S., Nagpal, K., Kohlberger, T., Dean, J., Corrado, G. S., Hipp, J. D., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0539-7 · Cited by 342 · see citing works

Deep learning-based transformation of H&E stained tissues into special stains

Haan, K. D., Zhang, Y., Zuckerman, J. E., Liu, T., Sisk, A. E. J., Diaz, M. F. P., Jen, K., Nobori, A., Liou, S. S., Zhang, S., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25221-2 · Cited by 326 · see citing works

Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping

Fu, H., Gong, M., Wang, C.-H., Batmanghelich, K. N., Zhang, K., & Tao, D. (2019) · 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.2019.00253 · Cited by 261 · see citing works

Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training

Mahmood, F., Chen, R., & Durr, N. J. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2842767 · Cited by 257 · see citing works

Adversarial Stain Transfer for Histopathology Image Analysis

BenTaieb, A., & Hamarneh, G. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2781228 · Cited by 231 · see citing works

A survey of methods for 3D histology reconstruction

Pichat, J., Iglesias, J. E., Yousry, T., Ourselin, S., & Modat, M. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.02.004 · Cited by 207 · see citing works

Creating Artificial Images for Radiology Applications Using Generative Adversarial Networks (GANs) – A Systematic Review

Sorin, V., Barash, Y., Konen, E., & Klang, E. (2020) · Academic Radiology · doi:10.1016/j.acra.2019.12.024 · Cited by 195 · see citing works

Comparison of Accuracy of Whole-Exome Sequencing with Formalin-Fixed Paraffin-Embedded and Fresh Frozen Tissue Samples

Oh, E., Choi, Y., Kwon, M. J., Kim, R. N., Kim, Y. J., Song, J., Jung, K. S., & Shin, Y. K. (2015) · PLoS ONE · doi:10.1371/journal.pone.0144162 · Cited by 115 · see citing works

TraVeLGAN: Image-To-Image Translation by Transformation Vector Learning

Amodio, M., & Krishnaswamy, S. (2019) · 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.2019.00919 · Cited by 110 · see citing works

Frozen Section Diagnoses of Small Pulmonary Nodules: Accuracy and Clinical Implications

Marchevsky, A. M., Changsri, C., Gupta, I., Fuller, C. B., Houck, W. V., & McKenna, R. J. (2004) · The Annals of Thoracic Surgery · doi:10.1016/j.athoracsur.2004.05.003 · Cited by 109 · see citing works

Accuracy of the IASLC/ATS/ERS histological subtyping of stage I lung adenocarcinoma on intraoperative frozen sections

Bittar, H. E. T., Incharoen, P., Althouse, A. D., & Đačić, S. (2015) · Modern Pathology · doi:10.1038/modpathol.2015.71 · Cited by 87 · see citing works

Turkish Population Structure and Genetic Ancestry Reveal Relatedness among Eurasian Populations

Hodoğlugil, U., & Mahley, R. W. (2012) · Annals of Human Genetics · doi:10.1111/j.1469-1809.2011.00701.x · Cited by 64 · see citing works

EndoL2H: Deep Super-Resolution for Capsule Endoscopy

Almalıoğlu, Y., Ozyoruk, K. B., Gökce, A., İncetan, K., Gokceler, G. I., Simsek, M. A., Ararat, K., Chen, R. J., Durr, N. J., Mahmood, F., et al. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3016744 · Cited by 62 · see citing works

Clinical implications of positive margins following non‐small cell lung cancer surgery

Predina, J. D., Keating, J. J., Patel, N., Nims, S., & Singhal, S. K. (2015) · Journal of Surgical Oncology · doi:10.1002/jso.24130 · Cited by 45 · see citing works

Diagnostic Accuracy of Cytology Smear and Frozen Section in Glioma

Zin, A. A. M., & Zulkarnain, S. (2019) · Asian Pacific Journal of Cancer Prevention · doi:10.31557/apjcp.2019.20.2.321 · Cited by 44 · see citing works

The diagnostic properties of frozen sections in suspected intracranial tumors: A study of 578 consecutive cases

Solheim, O. S., Tofte, K., Berger, C., & Torp, S. (2014) · Surgical Neurology International · doi:10.4103/2152-7806.146153 · Cited by 36 · see citing works

DeepLSR: a deep learning approach for laser speckle reduction

Bobrow, T. L., Mahmood, F., Inserni, M., & Durr, N. J. (2019) · Biomedical Optics Express · doi:10.1364/boe.10.002869 · Cited by 35 · see citing works

Accuracy of frozen section analysis in correlation with surgical pathology diagnosis

Patil, P., Shukla, S., Bhake, A. S., & Hiwale, K. (2015) · International Journal of Research in Medical Sciences · doi:10.5455/2320-6012.ijrms20150203 · Cited by 31 · see citing works

A generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sections

Falahkheirkhah, K., Guo, T., Hwang, M. J., Tamboli, P., Wood, C. G., Karam, J. A., Sircar, K., & Bhargava, R. (2021) · Laboratory Investigation · doi:10.1038/s41374-021-00718-y · Cited by 20 · see citing works

Accuracy of frozen-section diagnosis of brain tumors: an 11-year experience from a tertiary care center

Obeidat, F. N., Awad, H., Mansour, A. T., Hajeer, M., Al-jalabi, M. A., & Abudalu, L. (2018) · Turkish Neurosurgery · doi:10.5137/1019-5149.jtn.23220-18.2 · Cited by 19 · see citing works

Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain MappingPreprint

Fu, H., Gong, M., Wang, C., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.05852 · Cited by 18 · see citing works

On the acceptance of “fake” histopathology: A study on frozen sections optimized with deep learning

Siller, M., Stangassinger, L. M., Kreutzer, C., Boor, P., Bülow, R. D., Kraus, T. F. J., von Stillfried, S. F., Wolfl, S., Couillard‐Després, S., Oostingh, G. J., et al. (2022) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_53_21 · Cited by 13 · see citing works

An effective inflation treatment for frozen section diagnosis of small-sized lesions of the lung

Xiang, Z., Zhang, J., Zhao, J., Shao, J., Zhao, L., Zhang, Y., Qin, G., Xing, J., Han, Y., & Yu, K. (2020) · Journal of Thoracic Disease · doi:10.21037/jtd.2020.02.34 · Cited by 12 · see citing works

Cryoembedder, automatic processor/stainer, liquid nitrogen freezing, and manual staining for frozen section examination: A comparative study

Renne, S. L., Redaelli, S., & Paolini, B. (2019) · Acta Histochemica · doi:10.1016/j.acthis.2019.05.002 · Cited by 6 · see citing works

Frozen Section Diagnosis of Pediatric Brain Tumors

Adesina, A. M. (2010) · Surgical pathology clinics · doi:10.1016/j.path.2010.07.001 · Cited by 5 · see citing works

Towards a general-purpose foundation model for computational pathology

Chen, R. J., Ding, T., Lu, M. Y., Williamson, D. F. K., Jaume, G., Song, A. H., Chen, B., Zhang, A., Shao, D., Shaban, M., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02857-3 · Cited by 1,560 · see citing works

A whole-slide foundation model for digital pathology from real-world data

Xu, H., Usuyama, N., Bagga, J., Zhang, S., Rao, R., Naumann, T., Wong, C., Gero, Z., González, J., Gu, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07441-w · Cited by 921 · see citing works

Artificial intelligence for digital and computational pathology

Song, A. H., Jaume, G., Williamson, D. F. K., Lu, M. Y., Vaidya, A., Miller, T. R., & Mahmood, F. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00096-8 · Cited by 356 · see citing works

AI-enabled organoids: Construction, analysis, and application

Bai, L., Wu, Y., Li, G., Zhang, W., Zhang, H., & Su, J. (2023) · Bioactive Materials · doi:10.1016/j.bioactmat.2023.09.005 · Cited by 216 · see citing works

Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions

Lotter, W., Hassett, M. J., Schultz, N., Kehl, K. L., Van Allen, E. M., & Cerami, E. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1199 · Cited by 196 · see citing works

Rapid deep learning-assisted predictive diagnostics for point-of-care testing

Lee, S., Park, J., Woo, H., Yoo, Y. K., Lee, D., Chung, S. H., Yoon, D. S., Lee, K. B., & Lee, J. H. (2024) · Nature Communications · doi:10.1038/s41467-024-46069-2 · Cited by 123 · see citing works

Foundation models for fast, label-free detection of glioma infiltration

Kondepudi, A., Pekmezci, M., Hou, X., Scotford, K., Jiang, C., Rao, A. T., Harake, E. S., Chowdury, A., Al‐Holou, W. N., Wang, L., et al. (2024) · Nature · doi:10.1038/s41586-024-08169-3 · Cited by 119 · see citing works

Analysis of 3D pathology samples using weakly supervised AI

Song, A. H., Williams, M., Williamson, D. F. K., Chow, S. S. L., Jaume, G., Gao, G., Zhang, A., Chen, B., Baras, A. S., Serafin, R. J., et al. (2024) · Cell · doi:10.1016/j.cell.2024.03.035 · Cited by 108 · see citing works

Accelerating histopathology workflows with generative AI-based virtually multiplexed tumour profiling

Pati, P., Karkampouna, S., Bonollo, F., Compérat, É., Radić, M., Spahn, M., Martinelli, A. L., Wartenberg, M., Julio, M. K., & Rapsomaniki, M. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00889-5 · Cited by 93 · see citing works

Virtual formalin-fixed and paraffin-embedded staining of fresh brain tissue via stimulated Raman CycleGAN model

Liu, Z., Chen, L., Cheng, H., Ao, J., Xiong, J., Liu, X., Chen, Y., Mao, Y. Q., & Ji, M. (2024) · Science Advances · doi:10.1126/sciadv.adn3426 · Cited by 83 · see citing works

Machine learning in computational histopathology: Challenges and opportunities

Cooper, M. C., Ji, Z., & Krishnan, R. G. (2023) · Genes Chromosomes and Cancer · doi:10.1002/gcc.23177 · Cited by 81 · see citing works

Machine learning methods for histopathological image analysis: updates in 2024

Komura, D., Ochi, M., & Ishikawa, S. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.033 · Cited by 76 · see citing works

Progress and challenges of artificial intelligence in lung cancer clinical translation

Zhu, E., Muneer, A., Zhang, J., Yang, X., Li, X., Zhou, C., Heymach, J. V., Wu, J., & Le, X. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00986-7 · Cited by 52 · see citing works

Generative Artificial Intelligence in Anatomic Pathology

Brodsky, V., Ullah, E., Bychkov, A., Song, A. H., Walk, E. E., Louis, P., Rasool, G., Singh, R. S., Mahmood, F., Bui, M. M., et al. (2025) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2024-0215-ra · Cited by 48 · see citing works

A data-efficient strategy for building high-performing medical foundation models

Sun, Y., Tan, W., Gu, Z., He, R., Chen, S., Pang, M., & Yan, B. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01365-0 · Cited by 42 · see citing works

Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology

Mi, H., Sivagnanam, S., Ho, W. J., Zhang, S., Bergman, D. R., Deshpande, A. M., Baras, A. S., Jaffee, E. M., Coussens, L. M., Fertig, E. J., et al. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae421 · Cited by 38 · see citing works

Application of digital pathology‐based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response

Fu, X., Sahai, E. A., & Wilkins, A. C. (2023) · The Journal of Pathology · doi:10.1002/path.6153 · Cited by 36 · see citing works

Adaptive Dual-Axis Style-Based Recalibration Network With Class-Wise Statistics Loss for Imbalanced Medical Image Classification

Zhang, X., Xiao, Z., Ma, J., Wu, X., Zhao, J., Zhang, S., Li, R., Pan, Y. F., & Liu, J. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3551128 · Cited by 32 · see citing works

The role of unpaired image-to-image translation for stain color normalization in colorectal cancer histology classification

Altini, N., Marvulli, T. M., Zito, F. A., Caputo, M., Tommasi, S., Azzariti, A., Brunetti, A., Prencipe, B., Mattioli, E., Summa, S. D., et al. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107511 · Cited by 29 · see citing works

H&E to IHC virtual staining methods in breast cancer: an overview and benchmarking

Klöckner, P., Teixeira, J. A., Montezuma, D., Fraga, J., Horlings, H. M., Cardoso, J. S., & Oliveira, S. P. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01741-9 · Cited by 26 · see citing works

Unsupervised Multi-Domain Progressive Stain Transfer Guided by Style Encoding Dictionary

Guan, X., Wang, Y., Lin, Y., Li, X., & Zhang, Y. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3349866 · Cited by 24 · see citing works

Time for a full digital approach in nephropathology: a systematic review of current artificial intelligence applications and future directions

Cazzaniga, G., Rossi, M., Eccher, A., Girolami, I., L’Imperio, V., Nguyen, H. V., Becker, J. U., Bueno, G., Sbaraglia, M., Tos, A. P. D., et al. (2023) · Journal of Nephrology · doi:10.1007/s40620-023-01775-w · Cited by 21 · see citing works

Ultrabright NIR-IIb Fluorescence Quantum Dots for Targeted Imaging-Guided Surgery

Li, H., Du, Z., Zhu, L., Zhang, C., Xiong, J., Zhou, B., Dong, B., Zhang, X., & Alifu, N. (2024) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.4c04748 · Cited by 21 · see citing works

Virtual staining for pathology: Challenges, limitations and perspectives

Lin, W., Hu, Y., Zhu, R., Wang, B., & Wang, L. (2025) · Intelligent Oncology · doi:10.1016/j.intonc.2025.03.005 · Cited by 20 · see citing works

GenAI synthesis of histopathological images from Raman imaging for intraoperative tongue squamous cell carcinoma assessment

Yan, B., Wen, Z., Xue, L., Wang, T., Liu, Z., Long, W., Li, Y., & Jing, R. (2025) · International Journal of Oral Science · doi:10.1038/s41368-025-00346-y · Cited by 18 · see citing works

Whole slide images in artificial intelligence applications in digital pathology: challenges and pitfalls

Başak, K., Ozyoruk, K. B., & Demir, D. (2023) · Turkish Journal of Pathology · doi:10.5146/tjpath.2023.01601 · Cited by 18 · see citing works

A multicenter proof-of-concept study on deep learning-based intraoperative discrimination of primary central nervous system lymphoma

Zhang, X., Zhao, Z., Wang, R., Chen, H., Zheng, X., Liu, L., Lan, L., Li, P., Wu, S., Cao, Q., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48171-x · Cited by 17 · see citing works

Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma

Roetzer-Pejrimovsky, T., Nenning, K., Kiesel, B., Klughammer, J., Rajchl, M., Baumann, B., Langs, G., & Wöehrer, A. (2024) · GigaScience · doi:10.1093/gigascience/giae057 · Cited by 16 · see citing works

Accelerating Cancer Histopathology Workflows with Chemical Imaging and Machine Learning

Falahkheirkhah, K., Mukherjee, S. S., Gupta, S., Herrera-Hernandez, L. P., McCarthy, M. R., Jiménez, R. E., Cheville, J. C., & Bhargava, R. (2023) · Cancer Research Communications · doi:10.1158/2767-9764.crc-23-0226 · Cited by 13 · see citing works

Advanced techniques and nanotechnologies for point-of-care testing

Secchi, V., Armanni, A., Barbieri, L., Bruno, A., Colombo, A., Fumagalli, S., Kukushkina, E. A., Lorenzi, R., Marchesi, L., Moukham, H., et al. (2025) · Frontiers in Nanotechnology · doi:10.3389/fnano.2024.1465429 · Cited by 13 · see citing works

HisynSeg: Weakly-Supervised Histopathological Image Segmentation via Image-Mixing Synthesis and Consistency Regularization

Fang, Z., Wang, Y., Xie, P., Wang, Z., & Zhang, Y. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3520129 · Cited by 12 · see citing works

Path2Omics Enhances Transcriptomic and Methylation Prediction Accuracy from Tumor Histopathology

Hoang, D.-T., Shulman, E. D., Dhruba, S. R., Nair, N. U., Barman, R. K., Cantore, T., Biswas, S., Lalchungnunga, H., Singh, O., Chung, Y., et al. (2025) · Cancer Research · doi:10.1158/0008-5472.can-25-4350 · Cited by 11 · see citing works

Artificial intelligence in histopathological image analysis of central nervous system tumours: A systematic review

Jensen, M. P., Qiang, Z., Khan, D. Z., Stoyanov, D. V., Baldeweg, S. E., Jaunmuktane, Z., Brandner, S., & Marcus, H. J. (2024) · Neuropathology and Applied Neurobiology · doi:10.1111/nan.12981 · Cited by 11 · see citing works

High-Resolution Medical Image Translation via Patch Alignment-Based Bidirectional Contrastive Learning

Zhang, W., Hui, T. H., Tse, P. Y., Hill, F., Lau, C., & Li, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72083-3_17 · Cited by 11 · see citing works

Unsupervised medical image synthesis based on multi-branch attention structure

Hu, Y., Zhang, S., Li, W., Sun, J., & Xu, L. X. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107495 · Cited by 10 · see citing works

Enhanced multiscale human brain imaging by semi-supervised digital staining and serial sectioning optical coherence tomography

Cheng, S., Chang, S., Li, Y., Novoseltseva, A., Lin, S., Wu, Y., Zhu, J., McKee, A. C., Rosene, D. L., Wang, H., et al. (2025) · Light Science & Applications · doi:10.1038/s41377-024-01658-0 · Cited by 8 · see citing works

PathOmCLIP: Connecting tumor histology with spatial gene expression via locally enhanced contrastive learning of Pathology and Single-cell foundation modelPreprint

Lee, Y., Liu, X., Hao, M., Liu, T., & Regev, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.10.627865 · Cited by 7 · see citing works

CoLM: Contrastive learning and multiple instance learning network for lung cancer classification of surgical options based on frozen pathological images

Zhao, L., Zhao, W., Qiu, L., Jiang, M., Qian, L., Ting, H.-N., Fu, X., Zhang, P., Han, Y., & Zhao, J. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107097 · Cited by 7 · see citing works

Artificial intelligence in surgical pathology – Where do we stand, where do we go?

Sagiv, C., Hadar, O., Najjar, A., & Pahnke, J. (2024) · European Journal of Surgical Oncology · doi:10.1016/j.ejso.2024.109541 · Cited by 9 · see citing works

Artificial Intelligence in Pathology: Advancing Large Models for Scalable Applications

Xiao, Z., Feng, B., Yang, J., Sun, G., Shen, Y., Xu, S., Yang, L., Xu, H., Zhang, M., & Wang, S. (2025) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-103123-095814 · Cited by 9 · see citing works

Contrastive learning enhances fairness in pathology artificial intelligence systems

Lin, S.-Y., Tsai, P.-C., Su, F.-Y., Chen, C.-Y., Li, F., Zhao, J., Ho, Y. Y., Lee, M. T., Healey, E., Lin, P.-J., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102527 · Cited by 11 · see citing works

An end-to-end multifunctional AI platform for intraoperative diagnosis

Zheng, X., Zheng, K., Wen, Y., Meng, J., Zhang, X., Wen, X., Zhao, Z., Zheng, C., Cai, X., Lin, J., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01808-7 · Cited by 7 · see citing works

F2FLDM: Latent Diffusion Models with Histopathology Pre-Trained Embeddings for Unpaired Frozen Section to FFPE Translation

Ho, M. M., Dubey, S., Chong, Y., Knudsen, B. S., & Taşdizen, T. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00430 · Cited by 6 · see citing works

M 3 Surv : Fusing Multi-slide and Multi-omics for Memory-augmented robust Survival prediction

Qu, M., Yang, G., Di, D., Gao, Y., Song, Y., & Fan, L. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103846 · Cited by 6 · see citing works

A Multicenter Study on Intraoperative Glioma Grading via Deep Learning on Cryosection Pathology

Liu, X., Sun, T., Chen, H., Wu, S., Cheng, H., Liu, X., Lai, Q., Wang, K., Chen, L., Lu, J., et al. (2025) · Modern Pathology · doi:10.1016/j.modpat.2025.100749 · Cited by 5 · see citing works

His‐MMDM: Multi‐Domain and Multi‐Omics Translation of Histopathological Images with Diffusion Models

Li, Z., Su, T., Zhang, B., Han, W., Zhang, S., Sun, G., Cong, Y., Chen, X., Qi, J., Wang, Y., et al. (2026) · Advanced Science · doi:10.1002/advs.202518066 · Cited by 5 · see citing works

Histology and Embryology 4.0

Kılıç, K. D. (2023) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-6684-9814-9.ch008 · Cited by 5 · see citing works

Multiplexed tumor profiling with generative AI accelerates histopathology workflows and improves clinical predictionsPreprint

Pati, P., Karkampouna, S., Bonollo, F., Compérat, É., Radić, M., Spahn, M., Martinelli, A. L., Wartenberg, M., Julio, M. K., & Rapsomaniki, M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.29.568996 · Cited by 5 · see citing works

AI-ADC: Channel and Spatial Attention-Based Contrastive Learning to Generate ADC Maps from T2W MRI for Prostate Cancer Detection

Ozyoruk, K. B., Harmon, S. A., Lay, N. S., Yılmaz, E. C., Bağcı, U., Citrin, D. E., Wood, B. J., Pinto, P., Choyke, P. L., & Türkbey, B. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14101047 · Cited by 4 · see citing works

AI-driven lateral flow immunoassay for point-of-care detection of cardiac biomarkers in acute myocardial infarction

Lu, Y., Peng, X., Yin, Z., Fan, X., Fan, J., Mi, Y., & Li, G. (2026) · Microchimica Acta · doi:10.1007/s00604-026-08054-y · Cited by 4 · see citing works

Generative Adversarial Networks for Stain Normalisation in Histopathology

Breen, J., Zucker, K., Allen, K., Ravikumar, N., & Orsi, N. M. (2024) · Springer eBooks · doi:10.1007/978-3-031-46238-2_11 · Cited by 4 · see citing works

H&E Style Translation Using CycleGAN for Deep Ultraviolet-Excitation Fluorescence Images of Pancreatic Endoscopic Ultrasound-Fine Needle Aspiration/Biopsy Toward Slide-Free Rapid Pathology

Koyama, Y., Nakao, R., Sato, J., Honda, M., Inamori, O., Tanaka, N., Morinaga, Y., Konishi, E., Harada, Y., Tanaka, H., et al. (2025) · ACTA HISTOCHEMICA ET CYTOCHEMICA · doi:10.1267/ahc.25-00007 · Cited by 3 · see citing works

A Multi-Perspective Self-Supervised Generative Adversarial Network for FS to FFPE Stain Transfer

Lin, Y., Wang, Y., Fang, Z., Li, Z., Guan, X., Jiang, D., & Zhang, Y. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3460795 · Cited by 3 · see citing works

Universal NIR-II fluorescence image enhancement via square and square root network

Yu, X., Shi, X., Tian, J., & Hu, Z. (2024) · Signal Processing · doi:10.1016/j.sigpro.2024.109818 · Cited by 3 · see citing works

Systematic Analysis of Factors Affecting Human Epidermal Growth Factor Receptor 2 Interpretation Consistency: Staining Protocols, Artificial Intelligence–Based Image Standardization, and Classification Criteria

Jiang, C., Li, M., Zheng, C., Yan, S., Kong, L., Wu, Y., Zhang, J., Chao, X., Cai, X., Feng, W., et al. (2025) · Laboratory Investigation · doi:10.1016/j.labinv.2025.104134 · Cited by 3 · see citing works

Correlated Multiple IHC Virtual Staining for Breast Histopathological Images

Guan, X., Zhang, Z., Wang, Y., Zhang, Y., Jiang, D., & Zhang, Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889239 · Cited by 2 · see citing works

NIR ‐ II Fluorescence Image Translation via Latent Space Disentanglement

Yu, X., Tian, J., & Hu, Z. (2025) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70028 · Cited by 2 · see citing works

Research on the Underlying Principles and Deep Learning Algorithms based on Image Style Conversion Techniques

Tan, X., & Tan, L. (2023) · Transactions on Computer Science and Intelligent Systems Research · doi:10.62051/2z2ngm94 · Cited by 2 · see citing works

Künstliche Intelligenz in der Pathologie: Status quo und Zukunftsperspektiven

Foersch, S., & Schulz, S. (2024) · Die Onkologie · doi:10.1007/s00761-024-01482-6 · Cited by 2 · see citing works

Universal NIR-II fluorescence image enhancement via covariance weighted attention network

Yu, X., Tian, J., & Hu, Z. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01533-3 · Cited by 1 · see citing works

NIR-II fluorescence image enhancement via multi-step modulation

Yu, X., Tian, J., & Hu, Z. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128728 · Cited by 1 · see citing works

PRINTER:Deformation-Aware Adversarial Learning for Virtual IHC Staining with In Situ Fidelity

Yuan, Y., Xue, B., Pu, B., Wang, C., & Jin, C. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755487 · Cited by 1 · see citing works

MCS-Stain: Boosting FFPE-to-HE Virtual Staining With Multiple Cell Semantics

Hu, Y., Du, Z., Lin, W., Yang, S., Yu, L., Zhang, G., & Wang, L. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3628174 · Cited by 1 · see citing works

A multimodal framework to identify molecular mechanisms driving patient group-associated morphology through the integration of spatial transcriptomics and whole slide imaging

Kulkarni, R., Maddox, A., Bailey, S., & Rao, A. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-025-00050-6 · Cited by 1 · see citing works

Integration of artificial intelligence in histopathological and radiological image analysis

Alkhalaf, A. K., Alkhareeb, S. A., & Alshammari, M. M. (2024) · International Journal of Health Sciences · doi:10.53730/ijhs.v8ns1.15010 · Cited by 1 · see citing works

Towards interpretable molecular and spatial analysis of the tumor microenvironment from digital histopathology images with HistoTME-v2Preprint

Patkar, S., Rosean, T. R., Patel, P. G., Harmon, S. A., Choyke, P. L., Jamaspishvili, T., & Türkbey, B. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.11.658673 · Cited by 2 · see citing works

Künstliche Intelligenz in der Pathologie: Status quo und Zukunftsperspektiven

Foersch, S., & Schulz, S. (2024) · best practice onkologie · doi:10.1007/s11654-024-00572-6

Exploring and optimizing therapeutic strategies for borderline ovarian tumors and low-grade serous ovarian cancer

Decker, K. D. (2024) · doi:10.33612/diss.1081510083

Leveraging Pre-trained Models for FF-to-FFPE Histopathological Image Translation

Zhang, Q., Li, J., Liao, P., Hu, J., Guan, T., Han, A., & He, Y. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822745

Learning content and style representation for NIR-II fluorescence image translation

Yu, X., Shi, X., Tian, J., & Hu, Z. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04075-x

iMDPath: Interpretable Multi-task Digital Pathology Model for Clinical Pathological Image Prediction and InterpretationPreprint

Chen, Q., Wang, Z., Xia, L., Shi, Y., Xu, B., Chai, J., Zhang, T., & Wang, C. (2025) · medRxiv · doi:10.1101/2025.04.13.25323912

Exploratory multi-cohort, multi-reader study on the clinical utility of a deep learning model for transforming cryosectioned to formalin-fixed, paraffin-embedded (FFPE) images in breast lesion diagnosis

Xue, C., Wu, Y., Cai, X., He, J., Zheng, C., Li, M., Luo, R., Song, L., Li, X., Feng, W., et al. (2025) · Breast Cancer Research · doi:10.1186/s13058-025-02064-z

Enhancing frozen histological section images using permanent-section-guided deep learning with nuclei attention

Yoshai, E., Goldinger, G., Kogan, T., Zakharov, A., Haifler, M., & Shaked, N. T. (2025) · Scientific Reports · doi:10.1038/s41598-025-12181-6

Coherent Raman scattering microscopy for cancer diagnosis

Ji, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-22253-5.00012-1

Machine Vision–Enabled Lateral Flow Immunoassay Using Functionalized Gold Nanoparticles for Point-of-Care Cardiac Biomarker DetectionPreprint

Lu, Y., Peng, X., Yin, Z. Y., Fan, X., Fan, J., Mi, Y., & Li, G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.22.683875

AI-augmented intraoperative decision-making workflows in diffuse midline glioma biopsy using cryosection pathology

Yuan, Y., Yue, Q., Wang, Y., Du, Z., Wang, X., Yan, F., Cheng, H., Chen, K., Wu, Z., Song, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-66853-y

Rethinking Misalignment in Virtual Staining: A Preliminary Study on Using STN-Guided Supervision at Training Time to Improve H&E-to-IHC Translation

Liao, I. Y., Fadlelbari, M. M. S., Khattab, M., Ahmed, A. A., & Pan, J. W. (2025) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3743093.3770927

Intraoperative biopsy imaging of lung cancer risk

Qian, S., Yang, L., Meng, J., Zhou, L., Han, T., Chen, L., Xi, G., Jiang, R., Wang, C., Niu, B., et al. (2026) · Communications Medicine · doi:10.1038/s43856-026-01406-y

Diagnosis of Lung Diseases in the Artificial Intelligence Era

Papavassiliou, K. A., Sofianidi, A. A., & Papavassiliou, A. G. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27041796

Transforming Histology into Virtual Multiplex Immunofluorescence to Decode Prognostic Spatial Immunity in Hepatocellular CarcinomaPreprint

Cai, L., Jiang, S., Liang, J., Liu, F., Zhang, B., Reitsam, N. G., Zeng, Q., Hu, Z., Ma, Y., Li, Z., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.25.707931

100-Second Large-Area Chemical Imaging by Integrated Four-Channel CARS Microscopy

Ju, W., Huang, Z., Li, S., Zhong, Y., Li, Y., Li, N., Yang, C., Huang, Y., Wang, S., Yan, S., et al. (2026) · Chemical & Biomedical Imaging · doi:10.1021/cbmi.6c00016

Fusing imaging and metabolic modeling via multimodal deep learning in ovarian cancer

Eftekhari, N., Verma, S., Saha, A., Zampieri, G., Sawan, S., Occhipinti, A., & Angione, C. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101594

Xstainer: A Novel Virtual Staining Tool Powered by Advanced Deep Learning Techniques

Kinali, F. N., Demir, D., Özyörük, K. B., Almalıoğlu, Y., Köse, E. Ç., Williamson, D. F. K., Serın, G., Keles, G. E., Kumbaracı, B. S., Utku, C., et al. (2026) · Advanced Intelligent Systems · doi:10.1002/aisy.70424

Artificial intelligence-enabled organoids and organ-on-a-chip for pioneering biomedical platforms

Zhao, Y., Xie, D., Liu, H., Peng, B., Li, X., Wang, X., Leng, Y., Liu, J., Yang, T., Li, H., et al. (2026) · Bio-Design and Manufacturing · doi:10.1631/bdm.2500394

A dual-branch generative model for enhancing pathological diagnosis using unpaired frozen sections and formalin-fixed and paraffin-embedded images

Zhang, Y., Liu, Q., Zhao, Z., Chen, Q., Zheng, S., & Bai, X. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111384

Global-local information fusion via contrastive learning and optimal transport for weakly paired H&E-to-IHC stain transfer

Zhou, W., Zhou, D., Zhang, Q., Zang, P., Jiang, G., Cong, F., Liang, C., Li, S., Lu, C., & Xu, H. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104879

A dual-branch generative model for enhancing pathological diagnosis using unpaired frozen sections and formalin-fixed and paraffin-embedded images

Zhang, Y., Liu, Q., Zhao, Z., Chen, Q., Zheng, S., & Bai, X. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111384

Exploratory multi-cohort, multi-reader study on the clinical utility of a deep learning model for transforming cryosectioned to formalin-fixed, paraffin-embedded (FFPE) images in breast lesion diagnosis

Xue, C., Wu, Y., Cai, X., He, J., Zheng, C., Li, M., Luo, R., Song, L., Li, X., Feng, W., et al. (2025) · Breast Cancer Research · doi:10.1186/s13058-025-02064-z

Deep Learning-based Frozen Section to FFPE TranslationPreprint

Ozyoruk, K. B., Can, S., Gokceler, G. I., Başak, K., Demir, D., Serın, G., Hacısalihoğlu, U. P., Kurtuluş, E., Darbaz, B., Lu, M. Y., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.11786 · Cited by 6 · see citing works

A generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sections

Falahkheirkhah, K., Guo, T., Hwang, M. J., Tamboli, P., Wood, C. G., Karam, J. A., Sircar, K., & Bhargava, R. (2021) · Laboratory Investigation · doi:10.1038/s41374-021-00718-y · Cited by 20 · see citing works

F2FLDM: Latent Diffusion Models with Histopathology Pre-Trained Embeddings for Unpaired Frozen Section to FFPE Translation

Ho, M. M., Dubey, S., Chong, Y., Knudsen, B. S., & Taşdizen, T. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00430 · Cited by 6 · see citing works