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

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

A generalizable pathology foundation model using a unified knowledge distillation pretraining framework

Ma, J., Guo, Z., Zhou, F., Wang, Y., Xu, Y., Li, J., Yan, F., Cai, Y., Zhu, Z., Jin, C., et al. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01488-4 · Cited by 94

SPIE-AAPM-NCI BreastPathQ Challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment

Petrick, N., Akbar, S., Kenny, H., Nofech‐Mozes, S., Sahiner, B., Gavrielides, M. A., Kalpathy–Cramer, J., Drukker, K., & Martel, A. L. (2021) · Journal of medical imaging · doi:10.1117/1.jmi.8.3.034501 · Cited by 38

Deep learning for evaluation of microvascular invasion in hepatocellular carcinoma from tumor areas of histology images

Chen, Q., Xiao, H., Gu, Y., Weng, Z., Wei, L., Li, B., Liao, B., Li, J., Lin, J., Hei, M., et al. (2022) · Hepatology International · doi:10.1007/s12072-022-10323-w · Cited by 38

Machine learning does not outperform traditional statistical modelling for kidney allograft failure prediction

Truchot, A., Raynaud, M., Kamar, N., Naesens, M., Legendre, C. M., Delahousse, M., Thaunat, O., Büchler, M., Crespo, M., Linhares, K., et al. (2022) · Kidney International · doi:10.1016/j.kint.2022.12.011 · Cited by 44

Imaging-based Machine-learning Models to Predict Clinical Outcomes and Identify Biomarkers in Pancreatic Cancer

Janssen, B. V., Verhoef, S., Wesdorp, N. J., Huiskens, J., de Boer, O. J., Marquering, H. A., Stoker, J., Geert Kazemier, M., & Besselink, M. G. (2021) · Annals of Surgery · doi:10.1097/sla.0000000000005349 · Cited by 35

Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis

Wang, S., Hou, Y., Li, X., Meng, X., Zhang, Y., & Wang, X. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.765435 · Cited by 41

Impact of artificial intelligence on pathologists’ decisions: an experiment

Meyer, J. M., Khademi, A., Têtu, B., Han, W., Nippak, P. M. D., & Remisch, D. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac103 · Cited by 39

Automated detection of premalignant oral lesions on whole slide images using convolutional neural networks

Liu, Y., Bilodeau, E. A., Pollack, B., & Batmanghelich, K. N. (2022) · Oral Oncology · doi:10.1016/j.oraloncology.2022.106109 · Cited by 36

Pathomic Features Reveal Immune and Molecular Evolution From Lung Preneoplasia to Invasive Adenocarcinoma

Chen, P., Rojas, F. R., Hu, X., Serrano, A. G., Zhu, B., Chen, H., Hong, L., Bandyoyadhyay, R., Aminu, M., Kalhor, N., et al. (2023) · Modern Pathology · doi:10.1016/j.modpat.2023.100326 · Cited by 35

A review on federated learning in computational pathology

Schoenpflug, L. A., Nie, Y., Sheikhzadeh, F., & Koelzer, V. H. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.10.037 · Cited by 41

Development of a machine-learning based voice disorder screening tool

Reid, J., Parmar, P., Lund, T., Aalto, D. K., & Jeffery, C. C. (2022) · American Journal of Otolaryngology · doi:10.1016/j.amjoto.2021.103327 · Cited by 35

Accuracy of GPT-4 in histopathological image detection and classification of colorectal adenomas

Laohawetwanit, T., Namboonlue, C., & Apornvirat, S. (2025) · Journal of Clinical Pathology · doi:10.1136/jcp-2023-209304 · Cited by 35

Predicting colorectal cancer microsatellite instability with a self-attention-enabled convolutional neural network

Chang, X., Wang, J., Zhang, G., Yang, M., Xi, Y., Xi, C., Chen, G., Nie, X., Meng, B., & Quan, X. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100914 · Cited by 35

Whole slide imaging (WSI) scanner differences influence optical and computed properties of digitized prostate cancer histology

Duenweg, S. R., Bobholz, S. A., Lowman, A. K., Stebbins, M. A., Winiarz, A., Nath, B., Kyereme, F., Iczkowski, K. A., & LaViolette, P. S. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100321 · Cited by 35

Artificial intelligence in wound care: diagnosis, assessment and treatment of hard-to-heal wounds: a narrative review

Rippon, M. G., Fleming, L. T., Chen, T., Rogers, A. A., & Ousey, K. J. (2024) · Journal of Wound Care · doi:10.12968/jowc.2024.33.4.229 · Cited by 42

A Regulatory Science Initiative to Harmonize and Standardize Digital Pathology and Machine Learning Processes to Speed up Clinical Innovation to Patients

Marble, H. D., Huang, R., Dudgeon, S. N., Lowe, A., Herrmann, M. D., Blakely, S., Leavitt, M. O., Isaacs, M., Hanna, M. G., Sharma, A., et al. (2020) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_27_20 · Cited by 35

Histopathological Images and Multi-Omics Integration Predict Molecular Characteristics and Survival in Lung Adenocarcinoma

Chen, L., Zeng, H., Yu, X., Huang, Y., Luo, Y., & Ma, X. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.720110 · Cited by 36

Artificial intelligence in renal pathology: Current status and future

Feng, C., & Liu, F. (2022) · Biomolecules and Biomedicine · doi:10.17305/bjbms.2022.8318 · Cited by 35

Enhancing gland segmentation in colon histology images using an instance-aware diffusion model

Sun, M., Wang, J., Gong, Q., & Huang, W. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107527 · Cited by 35

Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing

Haghofer, A., Fuchs‐Baumgartinger, A., Lipnik, K., Klopfleisch, R., Aubreville, M., Scharinger, J., Weißenböck, H., Winkler, S., & Bertram, C. (2023) · Scientific Reports · doi:10.1038/s41598-023-46607-w · Cited by 34

The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review

Grignaffini, F., Barbuto, F., Troiano, M., Piazzo, L., Simeoni, P., Mangini, F., Stefanis, C. D., Muda, A. O., Frezza, F., & Alisi, A. (2024) · Diagnostics · doi:10.3390/diagnostics14040388 · Cited by 38

Potential rapid intraoperative cancer diagnosis using dynamic full-field optical coherence tomography and deep learning: A prospective cohort study in breast cancer patients

Zhang, S., Yang, B., Yang, H., Zhao, J. X., Zhang, Y., Gao, Y., Monteiro, O., Zhang, K., Liu, B., & Wang, S. (2024) · 中国科学通报：英文版 · doi:10.1016/j.scib.2024.03.061 · Cited by 38

Longitudinal big biological data in the AI era

Mardinoğlu, A., Türkez, H., Shong, M., Srinivasulu, V. P., Nielsen, J., Palsson, B. Ø., Hood, L., & Uhlén, M. (2025) · Molecular Systems Biology · doi:10.1038/s44320-025-00134-0 · Cited by 36

Deep learning powers cancer diagnosis in digital pathology

He, Y., Zhao, H. P., & Wong, S. T. C. (2020) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2020.101820 · Cited by 37

Ethical issues in computational pathology

Sorell, T., Rajpoot, N., & Verrill, C. L. (2021) · Journal of Medical Ethics · doi:10.1136/medethics-2020-107024 · Cited by 35

Deep-Learning-Aided Detection of Mycobacteria in Pathology Specimens Increases the Sensitivity in Early Diagnosis of Pulmonary Tuberculosis Compared with Bacteriology Tests

Zaizen, Y., Kanahori, Y., Ishijima, S., Kitamura, Y., Yoon, H., Ozasa, M., Mukae, H., Bychkov, A., Hoshino, T., & Fukuoka, J. (2022) · Diagnostics · doi:10.3390/diagnostics12030709 · Cited by 34

A comprehensive survey of intestine histopathological image analysis using machine vision approaches

Jing, Y., Li, C., Du, T., Jiang, T., Sun, H., Yang, J., Shi, L., Gao, M., Grzegorzek, M., & Li, X. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107388 · Cited by 40

Cancer care at the time of the fourth industrial revolution: an insight to healthcare professionals’ perspectives on cancer care and artificial intelligence

Hesso, I., Kayyali, R., Dolton, D.-R., Joo, K., Zacharias, L., Charalambous, A., Lavdaniti, M., Stalika, E., Ajami, T., Acampa, W., et al. (2023) · Radiation Oncology · doi:10.1186/s13014-023-02351-z · Cited by 33

Weakly supervised detection and classification of basal cell carcinoma using graph-transformer on whole slide images

Yacob, F., Siarov, J., Villiamsson, K., Suvilehto, J. T., Sjöblom, L., Kjellberg, M., & Neittaanmäki, N. (2023) · Scientific Reports · doi:10.1038/s41598-023-33863-z · Cited by 33

Automatic discovery of clinically interpretable imaging biomarkers for Mycobacterium tuberculosis supersusceptibility using deep learning

Tavolara, T. E., Niazi, M. K. K., Ginese, M. L., Piedra-Mora, C., Gatti, D. M., Beamer, G., & Gürcan, M. N. (2020) · EBioMedicine · doi:10.1016/j.ebiom.2020.103094 · Cited by 33

The Potential of Artificial Intelligence to Detect Lymphovascular Invasion in Testicular Cancer

Ghosh, A., Sirinukunwattana, K., Alham, N. K., Browning, L., Colling, R. T., Protheroe, A. S., Protheroe, E. A., Jones, S., Aberdeen, A., Rittscher, J., et al. (2021) · Cancers · doi:10.3390/cancers13061325 · Cited by 33

Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology

Yoshida, H., & Kiyuna, T. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i21.2818 · Cited by 34

Artificial intelligence for solid tumour diagnosis in digital pathology

Klein, C., Zeng, Q., Arbaretaz, F., Devêvre, E., Caldéraro, J., Loménie, N., & Maiuri, M. C. (2021) · British Journal of Pharmacology · doi:10.1111/bph.15633 · Cited by 36

Disruptive innovations in the clinical laboratory: catching the wave of precision diagnostics

Khatab, Z., & Yousef, G. M. (2021) · Critical Reviews in Clinical Laboratory Sciences · doi:10.1080/10408363.2021.1943302 · Cited by 40

Artificial Intelligence in the Pathology of Gastric Cancer

Choi, S., & Kim, S. (2023) · Journal of the Korean Gastric Cancer Association · doi:10.5230/jgc.2023.23.e25 · Cited by 32

Deep Learning for the Pathologic Diagnosis of Hepatocellular Carcinoma, Cholangiocarcinoma, and Metastatic Colorectal Cancer

Jang, H., Go, J., Kim, Y.-H., & Lee, S. H. (2023) · Cancers · doi:10.3390/cancers15225389 · Cited by 33

Artificial intelligence-based digital pathology using H&E-stained whole slide images in immuno-oncology: from immune biomarker detection to immunotherapy response prediction

Zhang, J., Choi, H., Kim, Y., Park, J., Cho, S., Kim, E., Cho, A., Lee, J. Y., Choi, J., Low, C., et al. (2025) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2024-011346 · Cited by 34

HEAL: an automated deep learning framework for cancer histopathology image analysis

Wang, Y., Coudray, N., Zhao, Y., Li, F., Hu, C., Zhang, Y., Imoto, S., Tsirigos, A., Webb, G. I., Daly, R. J., et al. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab380 · Cited by 32

Machine Learning-Based Detection of Dengue from Blood Smear Images Utilizing Platelet and Lymphocyte Characteristics

Mayrose, H., Bairy, G. M., Sampathila, N., Belurkar, S., & Saravu, K. (2023) · Diagnostics · doi:10.3390/diagnostics13020220 · Cited by 32

Nondestructive 3D Pathology with Light-Sheet Fluorescence Microscopy for Translational Research and Clinical Assays

Liu, J. T. C., Glaser, A. K., Poudel, C., & Vaughan, J. C. (2023) · Annual Review of Analytical Chemistry · doi:10.1146/annurev-anchem-091222-092734 · Cited by 31

Deep learning to predict cervical lymph node metastasis from intraoperative frozen section of tumour in papillary thyroid carcinoma: a multicentre diagnostic study

Liu, Y., Lai, F., Lin, B., Gu, Y., Chen, L., Chen, G., Xiao, H., Luo, S., Pang, Y., Xiong, D., et al. (2023) · EClinicalMedicine · doi:10.1016/j.eclinm.2023.102007 · Cited by 35

Applied machine learning in hematopathology

Dehkharghanian, T., Mu, Y., Tizhoosh, H. R., & Campbell, C. J. V. (2023) · International Journal of Laboratory Hematology · doi:10.1111/ijlh.14110 · Cited by 33

Transforming diagnostics: The implementation of digital pathology in clinical laboratories

Bruce, C., Prassas, I., Mokhtar, M., Clarke, B. A., Youssef, E., Wang, C., & Yousef, G. M. (2024) · Histopathology · doi:10.1111/his.15178 · Cited by 31

Enhancing Cervical Cancer Classification: Through a Hybrid Deep Learning Approach Integrating DenseNet201 and InceptionV3

Sharma, A., & Parvathi, R. (2025) · IEEE Access · doi:10.1109/access.2025.3527677 · Cited by 39

An update on applications of digital pathology: primary diagnosis; telepathology, education and research

Zia, S., Yildiz-Aktas, I. Z., Zia, F., & Parwani, A. V. (2025) · Diagnostic Pathology · doi:10.1186/s13000-025-01610-9 · Cited by 37

Automated Diagnosis of Dental Diseases Using Deep Learning on Radiographic Images

Tarek, R., Elshenawy, A., Assadwy, M. I., & Madkour, M. A. (2025) · SN Computer Science · doi:10.1007/s42979-025-04264-y · Cited by 31

Interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology

Gorman, C., Punzo, D., Octaviano, I., Pieper, S. D., Longabaugh, W. J. R., Clunie, D., Kikinis, R., Fedorov, A., & Herrmann, M. D. (2023) · Nature Communications · doi:10.1038/s41467-023-37224-2 · Cited by 36

Digital cytology: Current status and future prospects

Saini, T., Bansal, B., & Dey, P. (2023) · Diagnostic Cytopathology · doi:10.1002/dc.25099 · Cited by 30

Enhanced Pathology Image Quality with Restore–Generative Adversarial Network

Rong, R., Wang, S., Zhang, X., Wen, Z., Cheng, X. W., Jia, L., Yang, D. M., Xie, Y., Zhan, X., & Xiao, G. (2023) · American Journal Of Pathology · doi:10.1016/j.ajpath.2022.12.011 · Cited by 31

Impact of stain variation and color normalization for prognostic predictions in pathology

Lin, S., Zhou, H., Watson, M. A., Govindan, R., Côté, R. J., & Yang, C. (2025) · Scientific Reports · doi:10.1038/s41598-024-83267-w · Cited by 33

Medical students and house officers’ perception, attitude and potential barriers towards artificial intelligence in Egypt, cross sectional survey

Allam, R. M., Abdelfatah, D., Khalil, M. I. M., Elsaieed, M. M., & Desouky, E. D. E. (2024) · BMC Medical Education · doi:10.1186/s12909-024-06201-8 · Cited by 36

Objective Diagnosis for Histopathological Images Based on Machine Learning Techniques: Classical Approaches and New Trends

Elazab, N., Soliman, H., El-Sappagh, S., Islam, S. M. R., & Elmogy, M. M. (2020) · Mathematics · doi:10.3390/math8111863 · Cited by 36

Joint categorical and ordinal learning for cancer grading in pathology images

Vuong, T. T. L., Kim, K., Song, B., & Kwak, J. T. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102206 · Cited by 34

From What to Why, the Growing Need for a Focus Shift Toward Explainability of AI in Digital Pathology

Border, S. P., & Sarder, P. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2021.821217 · Cited by 31

Contrastive Multiple Instance Learning: An Unsupervised Framework for Learning Slide-Level Representations of Whole Slide Histopathology Images without Labels

Tavolara, T. E., Gürcan, M. N., & Niazi, M. K. K. (2022) · Cancers · doi:10.3390/cancers14235778 · Cited by 34

A Novel Deep Learning Algorithm for Human Papillomavirus Infection Prediction in Head and Neck Cancers Using Routine Histology Images

Wang, R., Khurram, S. A., Walsh, H., Young, L. S., & Rajpoot, N. (2023) · Modern Pathology · doi:10.1016/j.modpat.2023.100320 · Cited by 28

Development and evaluation of multimodal AI for diagnosis and triage of ophthalmic diseases using ChatGPT and anterior segment images: protocol for a two-stage cross-sectional study

Peng, Z., Ma, R., Zhang, Y., Yan, M., Lu, J. L., Cheng, Q., Liao, J., Zhang, Y., Wang, J., Zhao, Y., et al. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1323924 · Cited by 29

A multi-resolution self-supervised learning framework for semantic segmentation in histopathology

Wang, H., Ahn, E., & Kim, J. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110621 · Cited by 31

Artificial Intelligence in Cervical Cancer Screening: Opportunities and Challenges

Dellino, M., Cerbone, M., d’Amati, A., Bochicchio, M. A., Laganà, A. S., Etrusco, A., Malvası, A., Vitagliano, A., Pinto, V., Cicinelli, E., et al. (2024) · AI · doi:10.3390/ai5040144 · Cited by 37

Progress on deep learning in digital pathology of breast cancer: a narrative review

Zhu, J., Liu, M., & Li, X. (2022) · Gland Surgery · doi:10.21037/gs-22-11 · Cited by 33

One label is all you need: Interpretable AI-enhanced histopathology for oncology

Tavolara, T. E., Su, Z., Gurcan, M. N., & Niazi, M. K. K. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.09.006 · Cited by 28

Triple‐negative breast cancer survival prediction using artificial intelligence through integrated analysis of tertiary lymphoid structures and tumor budding

Hou, X., Li, X., Han, Y., Xu, H., Xie, Y., Zhou, T., Xue, T., Qian, X., Li, J., Wang, C., et al. (2024) · Cancer · doi:10.1002/cncr.35261 · Cited by 28

Deep Learning–Powered Whole Slide Image Analysis in Cancer Pathology

Dang, C., Qi, Z., Xu, T., Gu, M., Chen, J., Wu, J. J., Lin, Y., & Qi, X. (2025) · Laboratory Investigation · doi:10.1016/j.labinv.2025.104186 · Cited by 32

A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study

Dudgeon, S. N., Wen, S., Hanna, M. G., Gupta, R., Amgad, M. T., Sheth, M., Marble, H. D., Huang, R., Herrmann, M. D., Szu, C. H., et al. (2021) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_83_20 · Cited by 30

Legal and ethical considerations of artificial intelligence in skin cancer diagnosis

Jobson, D., Mar, V., & Freckelton, I. R. (2021) · Australasian Journal of Dermatology · doi:10.1111/ajd.13690 · Cited by 31

Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy

Ruini, C., Schlingmann, S., Jonke, Ž., Avci, P., Padrón‐Laso, V., Neumeier, F., Kövesházi, I., Ikeliani, I. U., Patzer, K., Kunrad, E., et al. (2021) · Cancers · doi:10.3390/cancers13215522 · Cited by 28

Fast and label-free automated detection of microsatellite status in early colon cancer using artificial intelligence integrated infrared imaging

Gerwert, K. B., Schörner, S., Großerueschkamp, F., Kraeft, A., Schuhmacher, D., Sternemann, C., Feder, I. S., Wisser, S., Lugnier, C., Arnold, D., et al. (2023) · European Journal of Cancer · doi:10.1016/j.ejca.2022.12.026 · Cited by 29

Machine Learning Quantified Tumor-Stroma Ratio Is an Independent Prognosticator in Muscle-Invasive Bladder Cancer

Zheng, Q., Jiang, Z., Ni, X., Song, Y., Jiao, P., Wu, J., Xiong, L., Yuan, J., Wang, J., Jian, J., et al. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032746 · Cited by 28

Survival prediction of stomach cancer using expression data and deep learning models with histopathological images

Wei, T., Yuan, X., Gao, R., Johnston, L. J., Zhou, J. N., Wang, Y., Kong, W., Xie, Y., Zhang, Y., Xu, D., et al. (2022) · Cancer Science · doi:10.1111/cas.15592 · Cited by 30

Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images

Su, Z., Rezapour, M., Sajjad, U., Gürcan, M. N., & Niazi, M. K. K. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107607 · Cited by 29

Machine learning in the EU health care context: exploring the ethical, legal and social issues

Beriaín, Í. D. M., Sanz, B., & Moratinos, G. L. (2020) · Information Communication & Society · doi:10.1080/1369118x.2020.1719185 · Cited by 30

Creating learning health systems and the emerging role of biomedical informatics

Kohn, M. S., Topaloĝlu, Ü., Kirkendall, E. S., Dharod, A., Wells, B. J., & Gürcan, M. N. (2021) · Learning Health Systems · doi:10.1002/lrh2.10259 · Cited by 27

Looking for a perfect match: multimodal combinations of Raman spectroscopy for biomedical applications

Schie, I. W., Stiebing, C., & Popp, J. (2021) · Journal of Biomedical Optics · doi:10.1117/1.jbo.26.8.080601 · Cited by 27

Slide Over

Liu, Y., Levenson, R. M., & Jenkins, M. W. (2021) · American Journal Of Pathology · doi:10.1016/j.ajpath.2021.10.010 · Cited by 28

Pathological prognosis classification of patients with neuroblastoma using computational pathology analysis

Liu, Y., Jia, Y., Hou, C., Li, N., Zhang, N., Yan, X., Yang, L., Guo, Y. X., Chen, H., Li, J., et al. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105980 · Cited by 27

The need for measurement science in digital pathology

Romanchikova, M., Thomas, S. A., Dexter, A., Shaw, M., Partarrieau, I., Smith, N. A. S., Venton, J., Adeogun, M., Brettle, D. S., & Turpin, R. J. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100157 · Cited by 27

CX-Net: an efficient ensemble semantic deep neural network for ROI identification from chest-x-ray images for COPD diagnosis

Ikechukwu, A. V., & Murali, S. (2023) · Machine Learning Science and Technology · doi:10.1088/2632-2153/acd2a5 · Cited by 34

K-PathVQA: Knowledge-Aware Multimodal Representation for Pathology Visual Question Answering

Naseem, U., Khushi, M., Dunn, A. G., & Kim, J. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3294249 · Cited by 30

SSLP: Spatial Guided Self-supervised Learning on Pathological Images

Li, J., Lin, T., & Xu, Y. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-87196-3_1 · Cited by 26

Application of Photoactive Compounds in Cancer Theranostics: Review on Recent Trends from Photoactive Chemistry to Artificial Intelligence

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