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

Knowledge and Perception of the Use of AI and its Implementation in the Field of Radiology: Cross-Sectional Study

Catalina, Q. M., Femenia, J., Fuster‐Casanovas, A., Marin-Gomez, F. X., Escalé-Besa, A., Solé‐Casals, J., & Vidal‐Alaball, J. (2023) · Journal of Medical Internet Research · doi:10.2196/50728 · Cited by 23

Revolutionizing Pathology with Artificial Intelligence: Innovations in Immunohistochemistry

Poalelungi, D. G., Neagu, A., Fulga, A., Neagu, M., Tutunaru, D., Nechita, A., & Fulga, I. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14070693 · Cited by 25

RI-ViT: A Multi-Scale Hybrid Method Based on Vision Transformer for Breast Cancer Detection in Histopathological Images

Monjezi, E., Akbarizadeh, G., & Ansari-Asl, K. (2024) · IEEE Access · doi:10.1109/access.2024.3514322 · Cited by 27

Convergence of Digital Pathology and Artificial Intelligence Tools in Anatomic Pathology Practice: Current Landscape and Future Directions

Parwani, A. V., & Amin, M. B. (2020) · Advances in Anatomic Pathology · doi:10.1097/pap.0000000000000271 · Cited by 23

Applications of Machine Learning in Cardiac Electrophysiology

Muthalaly, R. G., Monash Health, M., Evans, R. M., & Monash Health, M. (2020) · Arrhythmia & Electrophysiology Review · doi:10.15420/aer.2019.19 · Cited by 23

A Review of Artificial Intelligence in Precise Assessment of Programmed Cell Death-ligand 1 and Tumor-infiltrating Lymphocytes in Non−Small Cell Lung Cancer

Wu, J., & Lin, D. (2021) · Advances in Anatomic Pathology · doi:10.1097/pap.0000000000000322 · Cited by 24

Advantages of Using a Web-based Digital Platform for Kidney Preimplantation Biopsies

Neri, F., Eccher, A., Rigotti, P., Girolami, I., Zaza, G., Gambaro, G., Mastrosimini, M. G., Bencini, G., Bella, C. D., Mescoli, C., et al. (2021) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_23_21 · Cited by 23

Understanding the ethical and legal considerations of Digital Pathology

Coulter, C., McKay, F., Hallowell, N., Browning, L., Colling, R. T., Macklin, P. S., Sorell, T., Aslam, M., Bryson, G., Treanor, D. E., et al. (2021) · The Journal of Pathology Clinical Research · doi:10.1002/cjp2.251 · Cited by 23

Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives

Distante, A., Marandino, L., Bertolo, R. G., Ingels, A., Pavan, N., Pecoraro, A., Marchioni, M., Carbonara, U., Erdem, S., Amparore, D., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13132294 · Cited by 28

Standardized Classification of Lung Adenocarcinoma Subtypes and Improvement of Grading Assessment Through Deep Learning

Lami, K., Ota, N., Yamaoka, S., Bychkov, A., Matsumoto, K., Uegami, W., Munkhdelger, J., Seki, K., Sukhbaatar, O., Attanoos, R. L., et al. (2023) · American Journal Of Pathology · doi:10.1016/j.ajpath.2023.07.002 · Cited by 22

Multi-Cell Type and Multi-Level Graph Aggregation Network for Cancer Grading in Pathology Images

Abbas, S. F., Vuong, T. T. L., Kim, K., Song, B., & Kwak, J. T. (2022) · Medical Image Analysis · doi:10.1016/j.media.2023.102936 · Cited by 24

T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence

Duenweg, S. R., Bobholz, S. A., Barrett, M. J., Lowman, A. K., Winiarz, A., Nath, B., Stebbins, M. A., Bukowy, J. D., Iczkowski, K. A., Jacobsohn, K. M., et al. (2023) · Cancers · doi:10.3390/cancers15184437 · Cited by 23

SelectStitch: Automated Frame Segmentation and Stitching to Create Composite Images from Otoscope Video Clips

Binol, H., Moberly, A. C., Niazi, M. K. K., Essig, G., Shah, J., Elmaraghy, C., Teknos, T. N., Taj‐Schaal, N., Yu, L., & Gürcan, M. N. (2020) · Applied Sciences · doi:10.3390/app10175894 · Cited by 23

Artificial Intelligence in Digital Pathology: What Is the Future? Part 1: From the Digital Slide Onwards

Giovagnoli, M. R., & Giansanti, D. (2021) · Healthcare · doi:10.3390/healthcare9070858 · Cited by 23

A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology

Huang, L., Li, Y., Pillar, N., Haran, T. K., Wallace, W. D., & Özcan, A. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01421-9 · Cited by 24

The artificial intelligence revolution in gastric cancer management: clinical applications

Li, R., Li, J., Wang, Y., Liu, X., Xu, W., Sun, R., Xue, B., Zhang, X., Ai, Y., Du, Y., et al. (2025) · Cancer Cell International · doi:10.1186/s12935-025-03756-4 · Cited by 25

Systematic review and meta-analysis of deep learning for MSI-H in colorectal cancer whole slide images

Li, H., Qin, J., Li, Z., Ouyang, R., Chen, Z., Huang, S., Qin, S., & Huang, Q. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01848-z · Cited by 28

Thor: a platform for cell-level investigation of spatial transcriptomics and histology

Zhang, P., Chen, W., Tran, T. H., Zhou, M., Carter, K. N., Kandel, I., Li, S., Hoi, X. P., Sun, Y., Lai, L., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62593-1 · Cited by 25

Polarization imaging-based radiomics approach for the staging of liver fibrosis

Yao, Y., Zhang, F., Wang, B., Wan, J., Si, L., Dong, Y., Zhu, Y., Liu, X., Chen, L., & Ma, H. (2022) · Biomedical Optics Express · doi:10.1364/boe.450294 · Cited by 22

Initial Responses to False Positives in AI-Supported Continuous Interactions: A Colonoscopy Case Study

van Berkel, N., Opie, J., Ahmad, O. F., Lovat, L. B., Stoyanov, D. V., & BLANDFORD, A. E. (2022) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3480247 · Cited by 22

Deep Learning Facilitates Distinguishing Histologic Subtypes of Pulmonary Neuroendocrine Tumors on Digital Whole-Slide Images

Ilié, M., Benzaquen, J., Tourniaire, P., Heeke, S., Ayache, N., Delingette, H., Long-Mira, É., Lassalle, S., Hamila, M., Fayada, J., et al. (2022) · Cancers · doi:10.3390/cancers14071740 · Cited by 22

Artificial intelligence to predict oncological outcome directly from hematoxylin and eosin-stained slides in urology

WESSELS, F., Kuntz, S., KRIEGHOFF-HENNING, E., SCHMITT, M., BRAUN, V., Worst, T. S., NEUBERGER, M., STEEG, M., Gaiser, T., FRÖHLING, S., et al. (2022) · Minerva Urology and Nephrology · doi:10.23736/s2724-6051.22.04758-9 · Cited by 23

Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects

Wang, Y., Wen, Q., Jin, L., & Chen, W. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11164918 · Cited by 24

The status of digital pathology and associated infrastructure within Alzheimer’s Disease Centers

Scalco, R., Hamsafar, Y., White, C. L., Schneider, J. A., Reichard, R. R., Prokop, S., Perrin, R. J., Nelson, P. T., Mooney, S. D., Lieberman, A. P., et al. (2023) · Journal of Neuropathology & Experimental Neurology · doi:10.1093/jnen/nlac127 · Cited by 25

Development of Prognostic Biomarkers by TMB-Guided WSI Analysis: A Two-Step Approach

Liu, X., Liu, Z., Yan, Y., Wang, K., Wang, A., Ye, X., Wang, L., Wei, W., Li, B., Sun, C., et al. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3249354 · Cited by 23

Analysis of super-enhancer using machine learning and its application to medical biology

Hamamoto, R., Takasawa, K., Shinkai, N., Machino, H., Kouno, N., Asada, K., Komatsu, M., & Kaneko, S. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad107 · Cited by 21

Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images

Li, H., Cao, J., Grzybowski, A. E., Jin, K., Lou, L., & Ye, J. (2023) · Healthcare · doi:10.3390/healthcare11121739 · Cited by 24

ChatGPT as an aid for pathological diagnosis of cancer

Malik, S., & Zaheer, S. (2023) · Pathology - Research and Practice · doi:10.1016/j.prp.2023.154989 · Cited by 22

Applications of Digital Pathology in Cancer: A Comprehensive Review

Omar, M., Alexanderani, M. K., Valencia, I., Loda, M. F., & Marchionni, L. (2024) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-062822-010523 · Cited by 24

The prediction of pCR and chemosensitivity for breast cancer patients using DLG3, RADL and Pathomics signatures based on machine learning and deep learning

Jiang, C. F., Zhang, X., Qu, T., Yang, X., Xiu, Y., Yu, X. Y., Zhang, S., Qiao, K., Meng, H., Li, X., et al. (2024) · Translational Oncology · doi:10.1016/j.tranon.2024.101985 · Cited by 21

Label-free optical microscopy with artificial intelligence: a new paradigm in pathology

Yoon, C., Park, E., Kim, D., Park, B., & Kim, C. (2025) · Biophotonics discovery. · doi:10.1117/1.bios.2.2.020901 · Cited by 22

Automatic Diagnostic Tool for Predicting Cancer Grade in Bladder Cancer Patients Using Deep Learning

Wetteland, R., Kvikstad, V., Eftestøl, T., Tøssebro, E., Lillesand, M., Janssen, E. A. M., & Engan, K. (2021) · IEEE Access · doi:10.1109/access.2021.3104724 · Cited by 21

Convolutional neural network-based system for endocytoscopic diagnosis of early gastric cancer

Noda, H., Kaise, M., Higuchi, K., Koizumi, E., Yoshikata, K., Habu, T., Kirita, K., Onda, T., Omori, J., Akimoto, T., et al. (2022) · BMC Gastroenterology · doi:10.1186/s12876-022-02312-y · Cited by 21

Grad-CAM++ is Equivalent to Grad-CAM With Positive Gradients

Lerma, M., & Lucas, M. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/awjv6348 · Cited by 26

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

Predicting 5-year recurrence risk in colorectal cancer: development and validation of a histology-based deep learning approach

Han, X. G., Weng, Z., Sun, K., Shen, J., Lin, J., Chen, S., Ли, Б., Shi, Y., Kuang, M., Song, X., et al. (2024) · British Journal of Cancer · doi:10.1038/s41416-024-02573-2 · Cited by 24

ProDiv: Prototype-driven consistent pseudo-bag division for whole-slide image classification

Yang, R., Liu, P., & Ji, L. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108161 · Cited by 22

Artificial intelligence-based digital pathology for the detection and quantification of soil-transmitted helminths eggs

Cure-Bolt, N., Perez, F., Broadfield, L. A., Levecke, B., Hu, P., Oleynick, J., Beltrán, M., Ward, P., & Stuyver, L. J. (2024) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0012492 · Cited by 21

3D-Printed Portable Robotic Mobile Microscope for Remote Diagnosis of Global Health Diseases

García-Villena, J., Torres, J. E., Aguilar, C., Lin, L., Bermejo-Peláez, D., Dacal, E., Mousa, A., Ortega, M. D. P., Martínez, Á., Vladimirov, A. P., et al. (2021) · Electronics · doi:10.3390/electronics10192408 · Cited by 21

Intraoperative margin assessment for basal cell carcinoma with deep learning and histologic tumor mapping to surgical site

Levy, J. J., Davis, M. J., Chacko, R. S., Davis, M. J., Fu, L. J., Goel, T., Pamal, A., Nafi, I., Angirekula, A., Suvarna, A., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-023-00477-7 · Cited by 20

Artificial intelligence (AI) for tumor microenvironment (TME) and tumor budding (TB) identification in colorectal cancer (CRC) patients: A systematic review

Lobanova, O. A., Kolesnikova, A. O., Ponomareva, V. A., Vekhova, K. A., Shaginyan, A. L., Semenova, A. B., Nekhoroshkov, D. P., Kochetkova, S. E., Kretova, N. V., Zanozin, A. S., et al. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100353 · Cited by 20

A dynamic uncertainty-aware ensemble model: Application to lung cancer segmentation in digital pathology

Salvi, M., Mogetta, A., Raghavendra, U., Gudigar, A., Acharya, U. R., & Molinari, F. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112081 · Cited by 21

Artificial intelligence in predicting EGFR mutations from whole slide images in lung Cancer: A systematic review and Meta-Analysis

Nguyen, M. H., Minh, L. H. N., Bui, A. T., & Le, N. Q. K. (2025) · Lung Cancer · doi:10.1016/j.lungcan.2025.108577 · Cited by 22

Artificial intelligence and computer-aided diagnosis in diagnostic decisions: 5 questions for medical informatics and human-computer interface research

Brunyé, T. T., Mitroff, S. R., & Elmore, J. G. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf123 · Cited by 22

Establishment and Clinical Application of an Artificial Intelligence Diagnostic Platform for Identifying Rectal Cancer Tumor Budding

Liu, S., Zhang, Y., Ju, Y., Li, Y., Kang, X., Yang, X., Niu, T., Xing, X., & Lu, Y. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.626626 · Cited by 20

Qualitative Histopathological Classification of Primary Bone Tumors Using Deep Learning: A Pilot Study

Tao, Y., Huang, X., Tan, Y., Wang, H., Jiang, W., Chen, Y., Wang, C., Luo, J., Liu, Z., Gao, K., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.735739 · Cited by 20

Deep Learning-Based Classification and Targeted Gene Alteration Prediction from Pleural Effusion Cell Block Whole-Slide Images

Ren, W., Zhu, Y., Wang, Q., Jin, H., Guo, Y.-Y., & Lin, D. (2023) · Cancers · doi:10.3390/cancers15030752 · Cited by 21

NRK-ABMIL: Subtle Metastatic Deposits Detection for Predicting Lymph Node Metastasis in Breast Cancer Whole-Slide Images

Sajjad, U., Rezapour, M., Su, Z., Tozbikian, G. H., Gürcan, M. N., & Niazi, M. K. K. (2023) · Cancers · doi:10.3390/cancers15133428 · Cited by 20

Artificial Intelligence in Pathomics and Genomics of Renal Cell Carcinoma

Knudsen, J. E., Rich, J. M., & Ma, R. (2023) · Urologic Clinics of North America · doi:10.1016/j.ucl.2023.06.002 · Cited by 23

Deep Learning for Histopathological Assessment of Esophageal Adenocarcinoma Precursor Lesions

Botros, M., de Boer, O. J., Guevara, B. C., Bekkers, E. J., Jansen, M., van der Wel, M. J., Sá‎nchez, C. I., & Meijer, S. L. (2024) · Modern Pathology · doi:10.1016/j.modpat.2024.100531 · Cited by 20

End-to-end reproducible AI pipelines in radiology using the cloud

Bontempi, D., Nürnberg, L., Pai, S., Krishnaswamy, D., Thiriveedhi, V., Hosny, A., Mak, R. H., Farahani, K., Kikinis, R., Fedorov, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51202-2 · Cited by 23

Development of an automated estimation of foot process width using deep learning in kidney biopsies from patients with Fabry, minimal change, and diabetic kidney diseases

Smerkous, D. D., Mauer, M. S., Tøndel, C., Svarstad, E., Gübler, M.-C., Nelson, R. G., Oliveira, J. P., Sargolzaeiaval, F., & Najafian, B. (2023) · Kidney International · doi:10.1016/j.kint.2023.09.011 · Cited by 23

Integrating computational pathology and multi-transcriptomics to characterize lung adenocarcinoma heterogeneity and prognostic modeling

Li, Z., Qiao, W., Yu, S., Fan, B., Yang, M., Wu, M., Qiu, F., & Wang, J. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000002639 · Cited by 21

Automated cell annotation and classification on histopathology for spatial biomarker discovery

Li, Z., Mirjahanmardi, S. H., Sali, R., Eweje, F. R., Gopaulchan, M., Kloker, L. H., Zhang, X., Li, G., Jiang, Y., & Li, R. (2025) · Nature Communications · doi:10.1038/s41467-025-61349-1 · Cited by 21

Diagnosis of Acute Leukemia by Multiparameter Flow Cytometry with the Assistance of Artificial Intelligence

Zhong, P., Hong, M., He, H., Zhang, J., Chen, Y., Wang, Z., Chen, P., & Ouyang, J. (2022) · Diagnostics · doi:10.3390/diagnostics12040827 · Cited by 19

Computational pathology to improve biomarker testing in breast cancer: how close are we?

Sajjadi, E., Frascarelli, C., Venetis, K., Bonizzi, G., Ivanova, M. D., Vago, G. G., Guerini‐Rocco, E., & Fusco, N. (2023) · European Journal of Cancer Prevention · doi:10.1097/cej.0000000000000804 · Cited by 19

HiTAIC: hierarchical tumor artificial intelligence classifier traces tissue of origin and tumor type in primary and metastasized tumors using DNA methylation

Zhang, Z., Lu, Y., Vosoughi, S., Levy, J., Christensen, B. C., & Salas, L. A. (2023) · NAR Cancer · doi:10.1093/narcan/zcad017 · Cited by 18

Bi-directional Feature Fusion Generative Adversarial Network for Ultra-high Resolution Pathological Image Virtual Re-staining

Sun, K., Chen, Z., Wang, G., Liu, J., Ye, X., & Jiang, Y. (2023) · 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/cvpr52729.2023.00380 · Cited by 19

Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection

Huang, J., Li, H., Wan, X., & Li, G. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01955 · Cited by 19

The Applications of Artificial Intelligence (AI)-Driven Tools in Virus-Like Particles (VLPs) Research

Laxmi, B., Devi, P. U. M., Thanjavur, N., & Viswanath, B. (2024) · Current Microbiology · doi:10.1007/s00284-024-03750-5 · Cited by 21

Shapley Values-Enabled Progressive Pseudo Bag Augmentation for Whole-Slide Image Classification

Yan, R., Sun, Q., Jin, C., Liu, Y., He, Y., Guan, T., & Chen, H. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3453386 · Cited by 22

Pathobiology of myocardial and cardiomyocyte injury in ischemic heart disease: Perspective from seventy years of cell injury research

Buja, L. M. (2024) · Experimental and Molecular Pathology · doi:10.1016/j.yexmp.2024.104944 · Cited by 21

Artificial intelligence techniques support nuclear medicine modalities to improve the diagnosis of Parkinson’s disease and Parkinsonian syndromes

Palumbo, B., Bianconi, F., Nuvoli, S. M. F., Spanu, A., & Fravolini, M. L. (2020) · Clinical and Translational Imaging · doi:10.1007/s40336-020-00404-x · Cited by 20

The utility of artificial intelligence in the assessment of prostate pathology

Egevad, L., Ström, P., Kartasalo, K., Olsson, H., Samaratunga, H., Delahunt, B., & Eklund, M. (2020) · Histopathology · doi:10.1111/his.14060 · Cited by 19

Artificial Intelligence-Based Quality Assessment of Histopathology Whole-Slide Images within a Clinical Workflow: Assessment of ‘PathProfiler’ in a Diagnostic Pathology Setting

Browning, L., Jesus, C., Malacrino, S., Guan, Y., White, K., Puddle, A., Alham, N. K., Haghighat, M., Colling, R. T., Birks, J., et al. (2024) · Diagnostics · doi:10.3390/diagnostics14100990 · Cited by 18

Multi-omics synergy in oncology: Unraveling the complex interplay of radiomic, genoproteomic, and pathological data

Luo, Y., Li, Y., Fang, M., Wang, S., Shao, L., Zou, R., Dong, D., Liu, Z., Wei, J., & Tian, J. (2024) · Intelligent Oncology · doi:10.1016/j.intonc.2024.10.003 · Cited by 18

Advancing Laboratory Medicine Practice With Machine Learning: Swift yet Exact

You, J., Seok, H. S., Kim, S., & Shin, H. (2024) · Annals of Laboratory Medicine · doi:10.3343/alm.2024.0354 · Cited by 22

Harnessing artificial intelligence for enhanced veterinary diagnostics: A look to quality assurance, Part I Model development

Pacholec, C., Flatland, B., Xie, H., & Zimmerman, K. (2024) · Veterinary Clinical Pathology · doi:10.1111/vcp.13401 · Cited by 19

A deep learning method and device for bone marrow imaging cell detection

Liu, J., Yuan, R., Li, Y., Zhou, L., Zhang, Z., Yang, J. J., & Li, X. (2022) · Annals of Translational Medicine · doi:10.21037/atm-22-486 · Cited by 18

Deep learning for locally advanced nasopharyngeal carcinoma prognostication based on pre- and post-treatment MRI

Li, S., Deng, Y., Hua, H., Li, S., Chen, X., Xie, B., Zhu, Z., Liu, R., Huang, J., & Tao, Z. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106785 · Cited by 18

Computational pathology in ovarian cancer

Oršulić, S., John, J., Walts, A. E., & Gertych, A. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.924945 · Cited by 18

Identification of technology frontiers of artificial intelligence-assisted pathology based on patent citation network

Zhang, T., Chen, J., Lü, Y., Yang, X., & Ouyang, Z. (2022) · PLoS ONE · doi:10.1371/journal.pone.0273355 · Cited by 18

Multi-Task Distributed Learning Using Vision Transformer With Random Patch Permutation

Park, S. J., & Ye, J. C. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3218783 · Cited by 18

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

Deep Learning-Based Recognition of Cervical Squamous Interepithelial Lesions

An, H., Ding, L., Ma, M., Huang, A., Gan, Y., Sheng, D., Jiang, Z., & Zhang, X. (2023) · Diagnostics · doi:10.3390/diagnostics13101720 · Cited by 18

Pathologists’ first opinions on barriers and facilitators of computational pathology adoption in oncological pathology: an international study

Swillens, J. E. M., Nagtegaal, I. D., Engels, S., Lugli, A., Hermens, R., & van der Laak, J. A. W. M. (2023) · Oncogene · doi:10.1038/s41388-023-02797-1 · Cited by 18

Instance-Aware Diffusion Model for Gland Segmentation in Colon Histology Images

Sun, M., Huang, W., & Zheng, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43987-2_64 · Cited by 18

Development and validation of a deep learning model for improving detection of nonmelanoma skin cancers treated with Mohs micrographic surgery

Tan, E., Lim, S. S., Lamont, D. L., Epstein, R. J., LIM, D. J., & Lin, F. P. Y. (2023) · JAAD International · doi:10.1016/j.jdin.2023.10.007 · Cited by 18

Morphological diversity of cancer cells predicts prognosis across tumor types

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