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

Region–patch aware soft-attention network for hepatocellular carcinoma histopathology: context-guided multi-view fusion with deep ROI supervision

Zhang, H., & Sui, L. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110329 · Cited by 1

Deep learning-powered quantification of endocrine cells and CD3+ T cells in the natural history of type 1 diabetes

Kang, S., Maya, N., MORILLO, M., OUTAR, M., Posgai, A. L., Lamb, D. G., Campbell‐Thompson, M., & Kim, S. (2026) · Diabetologia · doi:10.1007/s00125-026-06742-1 · Cited by 1

Advancing Nasopharyngeal Carcinoma Diagnosis: A Systematic Review of AI-Driven Machine Learning Techniques for CT, MRI, and WSI Imaging in Bioengineering

Abdullahi, M. K., Wadood, A. S., Nabi, M. S., Mansor, S. B., & Fauzi, M. F. A. (2026) · Radiation · doi:10.3390/radiation6020016 · Cited by 1

Dual-stream attention-guided learning for weakly supervised whole slide image classification

Cao, D., Cheng, H., Li, Y., Zhou, R., Zhang, X., Li, X., Li, B., Gu, X., Zhang, J., Liu, X., et al. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115272 · Cited by 1

Intelligent three-dimensional imaging in pathology: Applications and developments

Si, W., Zhao, F., Kuang, D., Zhao, Y., Liu, Y., Sun, M., Duan, Y. Q., Fei, P., & Wang, G. (2026) · Chinese Medical Journal · doi:10.1097/cm9.0000000000004176 · Cited by 1

On the current clinical applicability of artificial intelligence in pathology: An evidence-informed clinical readiness review

Marchevsky, A. M., & Walts, A. E. (2026) · Annals of Diagnostic Pathology · doi:10.1016/j.anndiagpath.2026.152677 · Cited by 1

Mapping the evolving landscape of artificial intelligence in pathology: A bibliometric analysis of research trends and emerging frontiers (2009-2025)

Chai, K., Wang, K., Chen, C., Zeng, S., Zhang, J. I.-X., & Duan, X. (2026) · Digital Health · doi:10.1177/20552076261464736 · Cited by 1

From cells to patients: Multiscale computational pathology in the era of foundation models and vision-language systems

Gao, Z., Ge, J., Lu, J., Wu, L., Clayton, H., Zhang, D., Liu, J., Machado, I. P., Chen, Y., Crispin‐Ortuzar, M., et al. (2026) · Intelligent Oncology · doi:10.1016/j.intonc.2026.100074 · Cited by 1

Artificial intelligence and personalised medicine in liver cancer

Matuschewski, N. J., Baker, W. D., Chapiro, J., & Caldéraro, J. (2026) · Journal of Hepatology · doi:10.1016/j.jhep.2026.04.017 · Cited by 1

Practical applications of computational pathology: bridging technology and clinical practice

Hanna, M. G., Rashidi, H., & Pantanowitz, L. (2026) · Pathology · doi:10.1016/j.pathol.2026.06.012 · Cited by 1

Manual, digital, and AI tumour-infiltrating lymphocyte scoring: a secondary analysis of the APHINITY randomised trial

Gonzalez, L. E. L., Giudici, F., Salgado, R., Yan, H., Rojo, F., Comerma, L., Wienert, S., Palacios, J., Kos, Z., de Haas, S. L., et al. (2026) · The Lancet Oncology · doi:10.1016/s1470-2045(26)00247-0 · Cited by 1

Statistical Methods for Assessing Diagnostic Agreement

Pilz, M. (2026) · Applied Sciences · doi:10.3390/app16178808 · Cited by 1

From glass to pixel: Field restructuring, habitus crisis, and the revaluation of data in contemporary anatomic pathology—a sociological and autoethnographic perspective

Alfici, H. D. (2026) · Academic Pathology · doi:10.1016/j.acpath.2026.100302 · Cited by 1

Implementing Molecular Pathology in a Developing Country

Matsushita, M. D. M. (2021) · Molecular pathology library · doi:10.1007/978-3-030-69170-7_7 · Cited by 1

Lessons from a breast cell annotation competition series for school pupils

Lu, W., Miligy, I. M., Minhas, F., Park, Y. S., Snead, D. R. J., Rakha, E. A., Verrill, C. L., & Rajpoot, N. (2022) · Scientific Reports · doi:10.1038/s41598-022-11782-9 · Cited by 1

Identification and Validation of Co-Expressed Immune-Related Gene Signature Affecting the Pattern of Immune Infiltrating in Esophageal Cancer

Dong, B., Cheng, R., Zeng, H., Chen, L., & Zhou, L. (2022) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/1386207325666220705105906 · Cited by 1

View Sub-sampling and Reconstruction for Efficient Light Field Compression

Chen, Y., Alain, M., & Smolić, A. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/swcc3620 · Cited by 1

Reality Analagous Synthetic Dataset Generation with Daylight Variance for Deep Learning Classification

Lee, T., McKeever, S., & Courtney, J. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/poya9239 · Cited by 1

Better prognostic markers for nonmuscle invasive papillary urothelial carcinomas

Kvikstad, V. (2022) · doi:10.31265/usps.161 · Cited by 1

SGDANet: Gland Instance Segmentation Based on Spatial and Geometric Dual-Path Attention Modules

Zhang, H., Jiang, D., & Li, R. (2022) · International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · doi:10.1109/prai55851.2022.9904289 · Cited by 1

inteligencia artifical en biomedicina

Prol-Castelo, G., Urda-García, B., Núñez‐Carpintero, I., Cirillo, D., & Valencia, A. (2022) · Ambiociencias · doi:10.18002/ambioc.i20.7484 · Cited by 3

A machine learning model for cancer staging, case study: uveal cancer

Hamdaoui, F., Sghaier, A., Bdioui, A., & Hmissa, S. (2023) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista59065.2023.10310635 · Cited by 1

Artificial intelligence in diagnostic and predictive pathology

Frascarelli, C., Fusco, N., & Vago, G. G. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13671-9.00018-1 · Cited by 1

Centralized Health Coordination System for Managing Medical Records

Yadav, E., & Singh, E. S. (2024) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2024.62687 · Cited by 1

Automatic analysis of nuclear features reveals a non-tumoral predictor of tumor grade in bladder cancer

Fahoum, I., Tsuriel, S., Rattner, D., Greenberg, A., Zubkov, A., Naamneh, R., Greenberg, O., Zemser‐Werner, V., Gitstein, G., Hagege, R. R., et al. (2024) · Diagnostic Pathology · doi:10.1186/s13000-024-01501-5 · Cited by 1

Machine Learning in Medical Diagnosis of Cancer

Raj, R., Jyoti, J., Singh, A., & Kumar, K. (2024) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct61223.2024.10544354 · Cited by 1

Multimodal integration of radiology and pathology signatures for distinguishing between aldosterone-producing adenomas and nonfunctional adrenal adenomas

Piao, Z., Liu, T., Yang, H., Meng, M., Shi, H., Gao, S., Xue, T., & Jia, Z. (2024) · Endocrine · doi:10.1007/s12020-024-03827-y · Cited by 1

Rearrange Anatomy Inputs Like LEGO Bricks: Applying InSSS-P and a Mobile-Dense Hybrid Network to Distill Vascular Significance From Retina OCT-Angiography

Chang, K.-J., Lin, T., Hsu, C., Hwang, D., Chen, S., Chiou, S., Li, C.-Y., Chen, L., Hsu, C.-R., & Chang, W.-H. (2024) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2024.3401348 · Cited by 1

A Revolution in Healthcare: AI-powered Cancer Imaging

Kumar, G., Verma, S., Malviya, R., Paliwal, S., & Narayan, C. V. (2024) · Current Cancer Therapy Reviews · doi:10.2174/0115733947304178240804182938 · Cited by 1

Neuron collinearity differentiates human hippocampal subregions: a validated deep learning approach

Oltmer, J., Williams, E. M., Groha, S., Rosenblum, E. W., Roy, J., Rodríguez, J. L., Perosa, V., Champion, S. N., Frosch, M. P., & Augustinack, J. C. (2024) · Brain Communications · doi:10.1093/braincomms/fcae296 · Cited by 1

Transformative AI in Biomedicine Analysis

Tiwari, N. K. (2024) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3693-9311-6.ch006 · Cited by 1

Enhancing Knee Osteoarthritis Detection and Severity Grading Using Deep Learning and DenseNet169

Thangamani, R., Vimaladevi, M., Maheswaran, S., Sreeka, G., Srinithi, A., & Aswin, S. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10724058 · Cited by 1

AER-Net: Attention-Enhanced Residual Refinement Network for Nuclei Segmentation and Classification in Histology Images

Cao, R., Qingbin, M., Tan, D., Wei, P.-J., Ding, Y., & Zheng, C.-H. (2024) · Sensors · doi:10.3390/s24227208 · Cited by 1

Machine learning and pathology: a historical perspective

Malpani, S., Paz, R., Mirzabeigi, Y., Mohammadi, S. M. K., Gasca, R., & Arora, H. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15504-8.00002-8 · Cited by 1

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

Parwani, A. V., & Li, Z. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95359-7.00007-8 · Cited by 2

Dijital Patoloji ve Yapay Zekâ: Gelişimi, Uygulama Örnekleri ve Gelecek Yönelimler

Çalık, N., Gökcan, M. T., Topuz, Y., & Varlı, S. (2024) · Türkiye Sağlık Enstitüleri Başkanlığı Dergisi · doi:10.5578/tusebdergisi.20240338 · Cited by 1

Role of e-health, m-health, and telemedicine or telehealth during the COVID-19 pandemic

Mondal, R. (2025) · International Journal of Community Medicine and Public Health · doi:10.18203/2394-6040.ijcmph20250651 · Cited by 1

MiniSAM: A Lightweight Segmentation Model with Pruning and Knowledge Distillation

Liu, J., Wang, Y., Zhang, C., Wang, D., Zheng, J., Zhou, J. H., Wang, Y., Wang, X., & Chai, Y. (2024) · International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI) · doi:10.1109/ricai64321.2024.10911515 · Cited by 1

Double–Multiplex Immunostainings for Immune Profiling of Invasive Breast Carcinoma: Emerging Novel Immune-Based Biomarkers

Theodorou, S. D. P., Ntostoglou, K., Nikas, I. P., Goutas, D., Georgoulias, V., Kittas, C. N., & Pateras, I. S. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26072838 · Cited by 1

Intervention-Based Mixup Augmentation for Multiple Instance Learning in Whole-Slide Image Survival Analysis

Tang, L.-Z., Zhang, Z., Yang, J., Feng, Y., Wang, Q., Sun, S., & Shao, H. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3592359 · Cited by 2

The world is only ten years old: The dawn of artificial intelligence in urologic oncology

Loughlin, K. R. (2020) · Urologic Oncology Seminars and Original Investigations · doi:10.1016/j.urolonc.2020.04.020 · Cited by 1

Artificial Intelligence in Subspecialties

Chang, A. C. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-823337-5.00008-1 · Cited by 1

AIM in Surgical Pathology

McGenity, C., Wright, A., & Treanor, D. E. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_278 · Cited by 1

Artificial intelligence in cataract: What’s new?

Bhattacharjee, K., Rehman, O., Venkatraman, V., & Bhattacharjee, H. (2022) · Global Journal of Cataract Surgery and Research in Ophthalmology · doi:10.25259/gjcsro_4_2022 · Cited by 1

On the Feasibility of Privacy-Secured Facial Authentication for low-power IoT Devices - Quantifying the Effects of Head Pose Variation on End-to-End Neural Face Recognition

Yao, W., Varkarakis, V., Lemley, J., & Corcoran, P. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/fevr2516 · Cited by 1

A machine vision system for avian song classification with CNN’s

Palma, G., Aquino, A., Monticelli, P., Verdade, L. M., Markham, C., & Moral, R. D. A. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/mhzn4111 · Cited by 1

Machine learning-based pathomics model to predict the infiltration of regulatory T cells and prognosis in isocitrate dehydrogenase-wild- type glioblastomaPreprint

Peng, S., Wang, X., Chen, J., Yang, M., Lan, H., Hong, J., & Zhang, M. (2023) · Research Square · doi:10.21203/rs.3.rs-3274237/v1 · Cited by 1

A Multi-Granularity Approach to Similarity Search in Multiplexed Immunofluorescence ImagesPreprint

Yu, J. S., Wu, Z., Mayer, A. T., Trevino, A. E., & Zou, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.26.568745 · Cited by 1

Mapping 3D Heterogeneity of Thyroid Tumors Using Micro-CT based RadiomicsPreprint

Tajbakhsh, K., Stanowska, O., Buljan, M., Neels, A., Perren, A. A., & Zboray, R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.13.658854 · Cited by 1

AFTG-Net: A Deep Attention-based Fusion Framework of Topological and Gradient Features for Pathological Image AnalysisPreprint

Akan, T., Gelir, F., Aishwarya, R., Bhuiyan, M. S., & Bhuiyan, M. A. N. (2025) · Research Square · doi:10.21203/rs.3.rs-6710077/v1 · Cited by 1

Chinese expert opinion on AI-based identification of lymph node metastases in solid tumors

Wang, X., Tang, H., Chen, Y., Zhang, X., Liu, X., Zhu, M., Ni, C.-R., & Yu, G. (2025) · Journal of Histotechnology · doi:10.1080/01478885.2025.2580157 · Cited by 1

Smartphones, artificial intelligence and digital histopathology take on basal cell carcinoma diagnosis

Lee, K., & Soyer, H. P. (2019) · British Journal of Dermatology · doi:10.1111/bjd.18374

Background and History

Hinders, M. K. (2020) · Springer eBooks · doi:10.1007/978-3-030-49395-0_1

Artificial intelligence for the study of colorectal cancer tissue slides

Formica, V., Morelli, C., Riondino, S., Renzi, N., Nitti, D., & Roselli, M. (2020) · Artificial Intelligence in Gastroenterology · doi:10.35712/aig.v1.i3.51

Patología molecular del cáncer

Doimi-García, F., Gonzales-Paredes, A., Velarde-Álvarez, R. D., & Oblitas, J. A. P. (2020) · Diagnóstico · doi:10.33734/diagnostico.v59i2.222

Artificial intelligent for speech reproduction of information and knowledge of ancient books

Tang, L., & Chong-yu, L. (2021) · International Conference on Artificial Intelligence and Smart Energy (ICAIS) · doi:10.1109/icais50930.2021.9395993

PAI-WSIT: a Comprehensive Curated Resource for Cancerous Pathology With Deep LearningPreprint

Zhou, C., Feng, X., Jin, Y., Gu, H. F., Zhao, Y., Teng, X., Guo, L., Ji, J., Jia, S., Xing, Y., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-495066/v1

Deep Learning for Prediction of Neoadjuvant Chemoradiotherapy Response in Locally Advanced Rectal Cancer: A Retrospective Multi-Cohort Pathomics StudyPreprint

Li, Z., Zhou, X., Zheng, J., Zhang, D., Wu, L., Feng, L., Ding, Y., Cheng, X., Zhao, K., Li, P., et al. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3859293

An Experiment on the Impact of Information on the Trust in Artificial Intelligence

Meyer, J. M., & Remisch, D. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77750-0_39

[Identifying Molecular Subtypes of Whole-Slide Image in Colorectal Cancer via Deep Learning].

Liao, J., Feng, X., Wang, Y.-H., & Guo, L. (2021) · PubMed Central · doi:10.12182/20210760501

AIM in Surgical Pathology

McGenity, C., Wright, A., & Treanor, D. E. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_278-1

ЦИФРОВА ПАТОЛОГІЯ В РОБОТІ МЕДИЧНОЇ ЛАБОРАТОРІЇ. / АНАЛІТИЧНИЙ ОГЛЯД

Dudin, O., Mintser, O. P., & Сулаєва, О. М. (2021) · Medical Informatics and Engineering · doi:10.11603/mie.1996-1960.2020.3.11608

GENOMIC DATA ANALYSIS: CONCEPTUAL FRAMEWORK FOR THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN PERSONALIZED TREATMENT OF ONCOLOGY PATIENTS

Luic, R. K.-D. A. L. (2021) · doi:10.33965/icwi_ac2021_202109r030

Developing a Clinical Workflow That Fits Your Needs

Hartman, D. J. (2021) · Springer eBooks · doi:10.1007/978-3-030-83332-9_4

Discovering the Role of Artificial Intelligence in K12

Nadi, A. H., Hossain, S. F. A., Safin, M. A., Sofin, M. R., & Akhter, R. (2021) · Advances in mobile and distance learning book series · doi:10.4018/978-1-7998-6829-3.ch018 · Cited by 1

Machine Learning Based Classification from Whole-Slide Histopathological Images Enables Reliable and Interpretable Diagnosis of Inverted Urothelial PapillomaPreprint

Shao, W., Cheng, M., Huang, Z., Han, Z., Wang, T., Antonio Lopez-beltran, B., Osunkoya, A. O., Zhang, J., Liang Cheng, C., & Huang, K. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3959161

Verifiably Committed Signatures Provably Secure in The Standard Complexity Model.

Zhu, H. (2003)

Surveys of soil and water from asymptomatic natural ecosystems in South Africa reveal a goldmine of Phytophthora diversity

Oh, E., Wingfield, M. J., Gryzenhout, M., Wingfield, B. D., & Burgess, T. I. (2012) · Murdoch University Research Portal

Transferability of Deep Learning Models for Focus Quality Assessment in Digital PathologyPreprint

Miles, A., Hosseini, M. S., Tang, S., Wang, Z., Damaskinos, S., & Plataniotis, K. N. (2021) · Research Square · doi:10.21203/rs.3.rs-1120682/v1

Virtual Gene Concept and a Corresponding Pragmatic Research Program in Genetical Data Science

Huminiecki, Ł. (2021) · Entropy · doi:10.3390/e24010017

Predicting Drug-Efficiency and Cancer Evolution Using an Automated Histological Classifification Based on Whole Slide ImagesPreprint

Pan, Y., Liao, X., Ruan, X., Deng, Y., Du, F., Yu, G., & Wu, X. (2022) · Research Square · doi:10.21203/rs.3.rs-1402911/v1

The role of digital pathology in enabling remote operations during the CVID-19 pandemic and beyond a pathologist's perspective for the future opportunity

Bashir, S., Arif, S., Zia, R., Mustafa, G., Raziq, K., Bangash, S. A., Farid, A., & Aslam, A. (2022) · Pakistan BioMedical Journal · doi:10.54393/pbmj.v5i4.396

Key references

(2020) · Elsevier eBooks · doi:10.1016/b978-0-12-823337-5.00025-1

A new approach assessing the quality of whole slide images by discovering specific issuesPreprint

Song, Y., Gao, Y., Liang, C., & Liu, Z. (2020) · doi:10.21203/rs.3.rs-20180/v1

An Integrated Digital Pathology Platform for Tumors Using Artificial Intelligence AnalysisPreprint

Zhou, C., Feng, X., Cai, H., Jin, Y., Gu, H. F., Zhao, Y., Teng, X., Guo, L., Ji, J., Jia, S., et al. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3935673

Application of Artificial Intelligence in Research on Cancer and Its Metastasis

Franc, B. L. (2022) · Springer eBooks · doi:10.1007/978-3-030-93084-4_74

Inteligência artificial no processo de diagnóstico: utilização de software para comparação de imagens e perspectivas futuras

Pinho, R. F. D. C. (2022) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.23.2022.tde-29062022-092036

An NLP approach to Image Analysis

Martinez, G. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/kfbi5107

High-Fidelity Face Swapping with Style Blending

Yang, X., Guo, Z., Xue, M., & Shi, Z. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/kupa8487 · Cited by 1

Classification of electromagnetic interference induced image noise in an analog video link

Purcell, A. W., & Eising, C. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/zanb2627 · Cited by 1

Pre- and Post-Operative Analysis of Planar Radiographs in Total Hip Replacement

Denton, O., Madden-McKee, C., Hill, J., Beverland, D., Dunne, N., & Lennon, A. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/exjl3727

A Data Augmentation and Pre-processing Technique for Sign Language Fingerspelling Recognition

Fowley, F., Rushe, E., & Ventresque, A. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/xbav3102

Beyond Social Distancing: Application of real-world coordinates in a multi-camera system with privacy protection

Ryan, F., Hu, F., Dietlmeier, J., O'Connor, N. E., & McGuinness, K. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/rtns4233

Acoustic Source Localization Using Straight Line Approximations

Bagchi, S., & de Fréin, R. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/ljrb7078

Influence of Magnification in Deep Learning Aided Image Segmentation in Histological Digital Image Analysis

McCombe, K. D., Craig, S., James, J. A., & Gault, R. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/rakl2135

Detection and Isolation of 3D Objects in Unstructured Environments

Couto, D. D., Butterfield, J., Murphy, A., Rafferty, K., & Coleman, J. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/afmz9460

Deep learning for histopathological subtyping and grading of lung adenocarcinomaPreprint

Lami, K., Ota, N., Yamaoka, S., Bychkov, A., Matsumoto, K., Uegami, W., Attanoos, R. L., Berezowska, S., Brčić, L., Cavazza, A., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.02.514811

Comparison of machine learning to deep learning for automated annotation of Gleason patterns in whole mount prostate cancer histologyPreprint

Duenweg, S. R., Brehler, M., Bobholz, S. A., Lowman, A. K., Winiarz, A., Kyereme, F., Nencka, A. S., Iczkowski, K. A., & LaViolette, P. S. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.10.516007

Image analysis of immunohistochemistry-based biomarkers in breast cancer

Burchhardt, J. (2022) · doi:10.53846/goediss-9552

Super-resolution reconstruction of medical image via depth residual network

Ding, J., Shu, Y., Sun, J., Zuo, C., & Chen, Q. (2022) · Advanced Optical Imaging Technologies · doi:10.1117/12.2642021

Artificial intelligence applications in predicting the behavior of gastrointestinal cancers in pathology

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