Digital pathology and artificial intelligence

Deep learning

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

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812

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

GAN（Generative Adversarial Nets）

淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507

The cancer genome atlas pan-cancer analysis project

The Cancer Genome Atlas Research Network, Weinstein, J. N., Collisson, E. A., Mills, G. B., Shaw, K. R. M., Ozenberger, B. A., Ellrott, K., Shmulevich, I., Sander, C., & Stuart, J. M. (2013) · Nature Genetics · doi:10.1038/ng.2764 · Cited by 9,092

Conditional Generative Adversarial Nets

Mirza, M., & Osindero, S. (2014) · arXiv · doi:10.48550/arxiv.1411.1784 · Cited by 8,863

Image Style Transfer Using Convolutional Neural Networks

Gatys, L. A., Ecker, A. S., & Bethge, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.265 · Cited by 6,025

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

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial NetworksPreprint

Zhu, J.-Y., Park, T., Isola, P., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.10593 · Cited by 2,278

Few-shot tumor bud segmentation using generative model in colorectal carcinoma

Su, Z., Chen, W., Leigh, P. J., Sajjad, U., Niu, S., Rezapour, M., Frankel, W. L., Gürcan, M. N., & Niazi, M. K. K. (2024) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.3006418 · Cited by 4

A machine learning algorithm improves the diagnostic accuracy of the histologic component of antibody mediated rejection (AMR-H) in cardiac transplant endomyocardial biopsies

Glass, M., Ji, Z., Davis, R., Pavlisko, E. N., DiBernardo, L. R., Carney, J., Fishbein, G. A., Luthringer, D., Miller, D. V., Mitchell, R. N., et al. (2024) · Cardiovascular Pathology · doi:10.1016/j.carpath.2024.107646 · Cited by 4

Machine Learning in Breast Cancer Diagnosis: A Comprehensive Analysis and Prediction Methodology

Derraz, Y., Korchiyne, R., Laraqui, A., Benassila, M., Ergouyeg, M., Cherrab, H., Benzyane, Z., Sbai, M., & Houssaini, Z. S. (2024) · International Conference on Wireless Networks and Mobile Communications (WINCOM) · doi:10.1109/wincom62286.2024.10657943 · Cited by 4

Digital and Computational Pathology Applications in Bladder Cancer: Novel Tools Addressing Clinically Pressing Needs

Lobo, J., Zein‐Sabatto, B., Lal, P., & Netto, G. J. (2024) · Modern Pathology · doi:10.1016/j.modpat.2024.100631 · Cited by 4

Pathology in the artificial intelligence era: practical insights for immunohistochemistry and molecular pathology

Wasinger, G., Koeller, M. C., & Compérat, É. (2025) · Diagnostic histopathology · doi:10.1016/j.mpdhp.2025.04.003 · Cited by 5

Multimodal prediction of tyrosine kinase inhibitors therapy outcomes in advanced EGFR-mutated NSCLC patients

Chai, X., Li, H., Yang, M., Zeng, J., Chen, G., Li, Y., Wang, W., Liu, Z., Li, K., Zhang, T., et al. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06956-8 · Cited by 5

DualPrompt-MedCap: A Dual-Prompt Enhanced Approach for Medical Image Captioning

Zhao, Y., Prasad, M., & Braytee, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04981-0_20 · Cited by 4

Impact of Magnification, Image Type, and Number on Convolutional Neural Network Performance in Differentiating Canine Large Cell Lymphoma From Non‐Lymphoma via Lymph Node Cytology

Pacholec, C., Xie, H., Curnin, J., Lin, A., & Zimmerman, K. (2025) · Veterinary Clinical Pathology · doi:10.1111/vcp.70056 · Cited by 4

Computational pathology in the age of artificial intelligence – embrace not fear

Tan-Garcia, A., Chua, T. H., & Leow, W. Q. (2025) · The Journal of Pathology Clinical Research · doi:10.1002/2056-4538.70049 · Cited by 4

Artificial intelligence enabled tumor diagnosis and treatment: Status, breakthroughs and challenges

Hu, Y., Liang, S., Zhou, X., Zeng, Z., Ding, J., & Hua, D. (2025) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2025.104963 · Cited by 5

The problem of segmenting global glomerulosclerosis in gigapixel histopathological images: the borderless glomeruli

Souza, L., da Silva, J. B. B., Mendonça, M., Nathan, J., Duarte, A. A., Sarder, P., dos‐Santos, W. L. C., & de Oliveira, L. F. L. (2025) · BMC Nephrology · doi:10.1186/s12882-025-04469-6 · Cited by 4

Diagnostic text-guided representation learning in hierarchical classification for pathological whole slide image

Li, J., Sun, Q., Yan, R., Wang, Y., Fu, Y., Wei, Y., Guan, T., Shi, H., He, Y., & Han, A. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103894 · Cited by 11

Deep learning-based multimodal pathogenomics integration for precision cancer prognosis

Feng, X., Song, G., Zhang, Y., Guo, L., Jiang, Y., Gong, W., Feng, Y., Xu, C., Yang, Y., & He, M. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-026-07682-5 · Cited by 4

Artificial intelligence-based pathological model for pan-cancer lymph node metastasis detection: a multicentre diagnostic study with retrospective and prospective validation

Wu, S., Hong, G., Wang, Y., Zeng, H., Lin, Z., Yang, J., Chen, J., Chen, H., Huang, Y., Hong, L., et al. (2026) · The Lancet Digital Health · doi:10.1016/j.landig.2025.100961 · Cited by 4

A novel staging system derived from natural language processing of pathology reports to predict prognostic outcomes of pancreatic cancer: a retrospective cohort study

Li, B., Wang, B., Zhuang, P., Cao, H., Wu, S., Tan, Z., Gao, S., Li, P., Jing, W., Shao, Z., et al. (2023) · International Journal of Surgery · doi:10.1097/js9.0000000000000648 · Cited by 5

Number of intraepithelial lymphocytes and presence of a subepithelial band in normal colonic mucosa differs according to stainings and evaluation method

Fiehn, A. K., Engel, P. J. H., Engel, U., Jepsen, D. N. M., Blixt, T., Rasmussen, J., Wildt, S., Cebula, W., Diac, A. R., & Munck, L. K. (2024) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2024.100374 · Cited by 4

HDA-Net: H&E and RGB Dual Attention Network for Nuclei Instance Segmentation

Im, Y.-H., Park, S.-H., & Lee, S. (2024) · IEEE Access · doi:10.1109/access.2024.3390726 · Cited by 4

A Chronological Overview of Using Deep Learning for Leukemia Detection: A Scoping Review

Rodriguez, J. R., Fernandez, S., Swartz, N., Alonge, A., Bhullar, F., Betros, T., Girdler, M., Patel, N., Adas, S., Cervone, A., et al. (2024) · Cureus · doi:10.7759/cureus.61379 · Cited by 4

Roadmap for providing and leveraging annotated data by cytologists in the PDAC domain as open data: support for AI-based pathology image analysis development and data utilization strategies

Kim, J.-K., Bae, S., Yoon, S.-M., & Jeong, S. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1346237 · Cited by 4

Current advancements of machine learning in healthcare

Deshmukh, S. S., Kulkarni, N., Manoharan, A., Vinarski, B. M., & Ramanujam, A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15504-8.00001-6 · Cited by 4

A machine learning approach to automate microinfarct and microhemorrhage screening in hematoxylin and eosin-stained human brain tissues

Oliveira, L. C., Chauhan, J., Chaudhari, A., Cheung, S.-C. S., Patel, V., Villablanca, A. C., Jin, L., DeCarli, C. S., Chuah, C., & Dugger, B. N. (2024) · Journal of Neuropathology & Experimental Neurology · doi:10.1093/jnen/nlae120 · Cited by 4

A Novel Dielectric Fingerprinting Tool for Histopathology Assessment Leveraging AI and RF: A Feasibility Study Using Gastrointestinal Tissues

Gaddam, S., Samaddar, P., Gopalakrishnan, K., Singh, M., Anvekar, P., Shivaram, S., Dey, S., Roy, S. K., Mitra, D., & Arunachalam, S. P. (2025) · IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology · doi:10.1109/jerm.2024.3523861 · Cited by 4

A Novel Framework for Whole-Slide Pathological Image Classification Based on the Cascaded Attention Mechanism

Liu, D., & Hu, B. (2025) · Sensors · doi:10.3390/s25030726 · Cited by 4

Segmentation of Non-Small Cell Lung Carcinomas: Introducing DRU-Net and Multi-Lens Distortion

Oskouei, S., Valla, M., Pedersen, A., Smistad, E., Dale, V. G., Høibø, M., Wahl, S. G. F., Haugum, M. D., Langø, T., Ramnefjell, M., et al. (2025) · Journal of Imaging · doi:10.3390/jimaging11050166 · Cited by 6

An integrated system from microscopy to AI for real-time object detection in endometrial cytology

Terasaki, M., Tanaka, S., Shimokawa, I., Toda, E., Takakuma, S., Tabata, R., Sakae, K., Kajimoto, Y., Kunugi, S., Shimizu, A., et al. (2025) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2025.100541 · Cited by 6

Digital pathology as a platform for primary diagnosis and augmentation via deep learning

Parwani, A. V. (2020) · Elsevier eBooks · doi:10.1016/b978-0-323-67538-3.00006-3 · Cited by 4

Multiplex immunohistochemistry in lymphoma pathology: a research tool for study of the immune microenvironment

Pugh, M., Akarka, A. U., Hunter, K. J., Dojcinov, S. D., & Marafioti, T. (2020) · Diagnostic histopathology · doi:10.1016/j.mpdhp.2020.07.002 · Cited by 4

Exploring the Role of AI in K12

Hossain, S. F. A., Nurunnabi, M., Nadi, A. H., Hasan, A. M., & Ahsan, F. T. (2021) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-7998-6480-6.ch008 · Cited by 4

Whole Slide Imaging: Applications

Mohanty, S. K., & Parwani, A. V. (2021) · Springer eBooks · doi:10.1007/978-3-030-83332-9_3 · Cited by 5

Development of a Fully Automated Method to Obtain Reproducible Lymphocyte Counts in Patients With Colorectal Cancer

Fiehn, A. K., Reiss, B., Gögenur, M., Bzorek, M., & Gögenür, I. (2022) · Applied immunohistochemistry & molecular morphology · doi:10.1097/pai.0000000000001041 · Cited by 4

Digital Histopathological Discrimination of Label-Free Tumoral Tissues by Artificial Intelligence Phase-Imaging Microscopy

Ganoza-Quintana, J. L., Arce-Diego, J. L., & Fanjul-Vélez, F. (2022) · Sensors · doi:10.3390/s22239295 · Cited by 4

Clinical application of machine learning‐based pathomics signature of gastric atrophy

Lan, Y., Han, B., Zhai, T., Xu, Q., Li, Z., Liu, M., Xue, Y., & Xu, H. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1289265 · Cited by 5

Artificial Intelligence in Diagnosis and Treatment

Öztatlıcı, M., Eroğlu, S., Öztatlıcı, H., & Göl, M. (2024) · Gaziantep Islam Science and Technology University · doi:10.46871/eams.1470170 · Cited by 5

Multi-Artifact Detection and Filtering in Digital Pathology Using Intrinsic Image Properties

Kanwar, V., Woicik, A., Dulken, B., Probert, C. S., & McCaw, Z. R. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635902 · Cited by 4

Trends Innovations Challenges in Employing Interdisciplinary Approaches to Biomedical Sciences

Ganeshkumar, M., Ayyadhury, S., & Murugan, E. (2024) · Springer eBooks · doi:10.1007/978-981-97-1777-4_20 · Cited by 4

A Bifocal Classification and Fusion Network for Multimodal Image Analysis in Histopathology

Bao, G., Graeber, M. B., & Wang, X. (2020) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv50220.2020.9305360 · Cited by 4

Epithelial Layer Estimation Using Curvatures and Textural Features for Dysplastic Tissue Detection

Adam, A., Rahman, A. H. A., Sani, N. S., Alyessari, Z. A. A., Mamat, N. J. Z., & Hasan, B. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.014599 · Cited by 4

Development of a multiplex immuno-oncology biomarker and digital pathology workflow for assessment of urothelial carcinoma

Xie, Y., Olkhov‐Mitsel, E., Alminawi, S., Slodkowska, E. A., & Downes, M. R. (2021) · Pathology - Research and Practice · doi:10.1016/j.prp.2021.153607 · Cited by 4

Fast and Scalable Image Search For HistologyPreprint

Chen, C., Lu, M. Y., Williamson, D. F. K., Chen, T., Schaumberg, A. J., & Mahmood, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.13587 · Cited by 4

Automated quantification and statistical assessment of proliferating cardiomyocyte rates in embryonic hearts

Bogomolovas, J., Zhang, Z., Wu, T., & Chen, J. (2022) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00483.2022 · Cited by 4

Establishment and evaluation of Voting algorithm-based internal quality control (ViQC), a patient-based real-time quality control

Liu, Y., Zheng, H., Zhang, W., Xu, Z., Yu, J., Song, H., Gu, C., & Chen, Y. (2024) · Clinica Chimica Acta · doi:10.1016/j.cca.2024.119821 · Cited by 3

Automatic maxillary sinus segmentation and age estimation model for the northwestern Chinese Han population

Guo, Y., Lan, J.-L., Bu, W.-Q., Tang, Y., Wu, D., Yang, H., Ren, J.-C., Song, Y., Yue, H.-Y., Guo, Y.-C., et al. (2025) · BMC Oral Health · doi:10.1186/s12903-025-05618-x · Cited by 3

Development and interpretation of a pathomics-based model for the prediction of immune therapy response in colorectal cancer

Luo, Y., Tian, Q., Xu, L., Zeng, D., Zhang, H., Zeng, T., Tang, H., Wang, C., & Chen, Y. (2025) · Methods · doi:10.1016/j.ymeth.2025.05.012 · Cited by 3

Artificial intelligence technologies in biomedical research on human adaptation and maladaptation to environmental factors

Balunov, I. O., Mikhalishchina, A. S., Venerin, A. А., & Glazachev, O. S. (2025) · Экология человека · doi:10.17816/humeco643537 · Cited by 5

Vision transformer network discovers the prognostic value of pancreatic cancer pathology sections via interpretable risk scores

Peng, Z., Zhang, Y., Zhou, T., Shi, W., Wang, Y. Q., Pech, M., Rose, G., Dölling, M., Hippe, K., Croner, R. S., et al. (2025) · Discover Oncology · doi:10.1007/s12672-025-03547-3 · Cited by 3

A quantitative study of pathologists’ perceptions towards artificial intelligence-assisted diagnostic system

Ye, Z., Qu, L., Wang, J., Jiang, Y., & Xue, P. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0001052 · Cited by 5

Modernizing Colorectal Cancer Care With Artificial Intelligence: Real-Time Detection, Radiomics, and Digital Pathology

Nasr, E., Al-Hamid, Z., Younan, M. H., Omran, M., Sarsam, M., & Abdellatif, M. (2025) · Cureus · doi:10.7759/cureus.95178 · Cited by 6

Artificial Intelligence in Histopathological Analysis for Predicting Immunotherapy Response in Cutaneous Melanoma

Yoo, S. A., & Lee, J. H. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms262110729 · Cited by 3

Clinical applications of artificial intelligence in the histopathology of lymphoma: diagnosis, treatment and prognosis

Kang, M., Yang, Z., Tian, Y., Li, D., Wang, Z., & Chen, L. (2025) · Discover Oncology · doi:10.1007/s12672-025-03970-6 · Cited by 3

Foundation models: Insights and implications for gastrointestinal cancer

Shi, L., Huang, R., Zhao, L.-L., & Guo, A.-J. (2025) · World Journal of Gastroenterology · doi:10.3748/wjg.v31.i47.112921 · Cited by 3

SlideInspect : From Pixel‐Level Artifact Detection to Actionable Quality Metrics in Digital Pathology

Scotto, M., Patti, R., L'Imperio, V., Fraggetta, F., Molinari, F., & Salvi, M. (2026) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70292 · Cited by 3

Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application

Wang, Y., Wang, J. C., & Chen, H. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-026-07746-6 · Cited by 4

Deep Learning for Analysis of Bone Marrow Adiposity: Breakthroughs from Recent Large-Scale Analyses in the UK Biobank

Xu, W., Wang, C., & Cawthorn, W. P. (2026) · Current Osteoporosis Reports · doi:10.1007/s11914-026-00953-6 · Cited by 3

Digital pathology and artificial intelligence in breast and gynecologic oncology: from molecular prediction to multimodal integration

Polit, F., Bahmad, H. F., Kassab, M. B., Elajami, M. K., Recine, M. A., Alghamdi, S., & Poppiti, R. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1833926 · Cited by 3

Artificial intelligence and the evolution of pathology: Scaling from digital diagnostics to multimodal precision medicine

Huynh, C. D. (2026) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2026.100386 · Cited by 4

Detection of acute lymphocytic leukemia (ALL) with a pre-trained deep learning model

Doğan, Ş., Taşçı, B., & Tuncer, T. (2023) · International Conference on Recent Academic Studies · doi:10.59287/icras.681 · Cited by 3

无透镜编码叠层显微成像原理及研究进展（特邀）

Guo, C., Jingyan, 李. L., Shaowei, 江. J., Xiaopeng, 邵. S., & Guoan, 郑. Z. (2024) · Laser & Optoelectronics Progress · doi:10.3788/lop232582 · Cited by 3

Leveraging pre-trained machine learning models for islet quantification in type 1 diabetes

Kang, S., Peñaloza-Aponte, J. D., Elashkar, O., Morales, J. F., Waddington, N., Lamb, D. G., Ju, H., Campbell‐Thompson, M., & Kim, S. (2024) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2024.100406 · Cited by 3

Trends and Challenges of the Modern Pathology Laboratory for Biopharmaceutical Research Excellence

Sisó, S., Kavirayani, A. M., Couto, S. S., Stierstorfer, B., Mohanan, S., Morel, C., Marella, M., Bangari, D. S., Clark, E. S., Schwartz, A. J., et al. (2024) · Toxicologic Pathology · doi:10.1177/01926233241303898 · Cited by 3

Neoplasms in the Nasal Cavity Identified and Tracked with an Artificial Intelligence-Assisted Nasal Endoscopic Diagnostic System

Xu, X., Yun, B., Zhao, Y., Jin, L., Zong, Y., Yu, G., Zhao, C., Fan, K. C., Zhang, X., Tan, S., et al. (2024) · Bioengineering · doi:10.3390/bioengineering12010010 · Cited by 3

The KMeansGraphMIL Model

Chen, L., Xiao, H., Jiang, J., Li, B., Liu, W., & Huang, W. (2025) · American Journal Of Pathology · doi:10.1016/j.ajpath.2024.12.008 · Cited by 3

Digital pathology and AI in forensic medicine: advances, limitations, and medico-legal utility

Singh, J. P., Tyagi, S., Sharma, A., Chanana, A., & Basra, S. (2025) · Academia Medicine · doi:10.20935/acadmed7799 · Cited by 5

Healthcare Delivery: Leveraging Artificial Intelligence to Strengthen Healthcare Quality

Seitzinger, P., Rafid-Hamed, Z., & Kalra, J. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-80744-3_3 · Cited by 3

Correlation of manual semi-quantitative and automated quantitative Ki-67 proliferative index with OncotypeDXTM recurrence score in invasive breast carcinoma

Finkelman, B. S., Meindl, A., LaBoy, C., Griffin, B. B., Narayan, S. P., Brancamp, R. L., Siziopikou, K. P., Pincus, J. L., & Blanco, L. Z. (2021) · Breast Disease · doi:10.3233/bd-201011 · Cited by 3

Vascular Lakes in Uveal Melanoma and Their Association With Outcome

Jones, H., Kalirai, H., Taktak, A. F. G., Chen, K., & Coupland, S. E. (2022) · Translational Vision Science & Technology · doi:10.1167/tvst.11.3.32 · Cited by 3

A multi-view deep learning model for pathology image diagnosis

Dong, W., Sun, S., & Yin, M. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03918-1 · Cited by 3

Advances in the application of computational pathology in diagnosis, immunomicroenvironment recognition, and immunotherapy evaluation of breast cancer: a narrative review

Luo, J., Li, X., Wei, K., Chen, G., & Xiong, D. (2023) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-023-05002-8 · Cited by 3

Point-Wise Spatial Network for Identifying Carcinoma at the Upper Digestive and Respiratory Tract

Zhou, L., Jiang, H., Li, G., Ding, J., Lv, C., Duan, M., Wang, W., Chen, K., Shen, N., & Huang, X. (2020) · BMC Medical Imaging · doi:10.1186/s12880-023-01076-5 · Cited by 4

Assessing Knowledge, Acceptance, and Anticipated Impact of Telepathology in Saudi Arabia: Insights From Healthcare Workers and Patients

Aljerian, K., Alrashedi, A., Alkulaibi, R., Alsuwailem, R. I., Alshahrani, A., Alzahrani, F. M., Alqazlan, N., & Temsah, M.-H. A. (2023) · Cureus · doi:10.7759/cureus.49218 · Cited by 3

Investigation of latent representation of toxicopathological images extracted by CNN model for understanding compound properties in vivo

Maedera, S., Mizuno, T., & Kusuhara, H. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107748 · Cited by 3

Role of artificial intelligence in cancer diagnostics and therapeutics

Singh, R. S., Masih, G. D., Joshi, R., Sharma, S., Singh, A. K., & Medhi, B. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95116-6.00015-3 · Cited by 3

Immunohistochemical double nuclear staining for cell-specific automated quantification of the proliferation index – A promising diagnostic aid for melanocytic lesions

Brogård, M. B., Nielsen, P. S., Christensen, K. B., Georgsen, J. B., Wandler, A., Lade‐Keller, J., & Steiniche, T. (2024) · Pathology - Research and Practice · doi:10.1016/j.prp.2024.155177 · Cited by 3

Detection of presence or absence of metastasis in WSI patches of breast cancer using the dual-enhanced convolutional ensemble neural network

Ge, R., Chen, G., Saruta, K., & Terata, Y. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100579 · Cited by 3

Utilizing deep learning model for assessing melanocytic density in resection margins of lentigo maligna

Siarov, J., Kumar, D., Paoli, J., Mölne, J., Gillstedt, M., & Neittaanmäki, N. (2024) · Diagnostic Pathology · doi:10.1186/s13000-024-01532-y · Cited by 3

HIPI: Spatially resolved multiplexed protein expression inferred from H&E WSIs

Zeira, R., Anavy, L., Yakhini, Z., Rivlin, E., & Freedman, D. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012501 · Cited by 5

The presence and location of podocytes in glomeruli as affected by diabetes mellitus

Maraszek, K. E., Santo, B. A., Yacoub, R., Tomaszewski, J. E., Mohammad, I., Worral, A. M., & Sarder, P. (2020) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2548904 · Cited by 3

Prediction of False-Positive Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Molecular Results in a High-Throughput Open-Platform System

Martinez, R. J., Pankratz, N., Schomaker, M. L., Daniel, J. J., Beckman, K. B., Karger, A. B., Thyagarajan, B., Ferreri, P., Yohe, S. L., & Nelson, A. (2021) · Journal of Molecular Diagnostics · doi:10.1016/j.jmoldx.2021.05.015 · Cited by 3

Comment on "Artificial intelligence to predict oncological outcome directly from hematoxylin and eosin-stained slides in urology: a systematic review"

BELENCHON, I. R., Checcucci, E., Rivas, J. G., Puliatti, S., Taratkin, M. S., Kowalewski, K., Rodler, S., Veccia, A., López, R. A. M., & Cacciamani, G. E. (2023) · Minerva Urology and Nephrology · doi:10.23736/s2724-6051.22.05180-1 · Cited by 3

Telepathology in Medical Education: Integration of Digital Microscopy in Distance Pathology Education during COVID-19 Pandemic

Şensu, S. (2020) · Journal of Medical Science And clinical Research · doi:10.18535/jmscr/v8i11.93 · Cited by 3

Whole-Slide Imaging: Updates and Applications in Papillary Thyroid Carcinoma

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