Machine Learning Methods for Histopathological Image Analysis

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380

A survey on deep learning in medical image analysis

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332

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

A comparative study of texture measures with classification based on featured distributions

Ojala, T., Pietikäinen, M., & Harwood, D. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00067-4 · Cited by 6,882

Deep learning in medical image analysis

Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968

Solving the multiple instance problem with axis-parallel rectangles

Dietterich, T. G., Lathrop, R. H., & Lozano-Pérez, T. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(96)00034-3 · Cited by 2,866

Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning

Miyato, T., Maeda, S., Koyama, M., & Ishii, S. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2858821 · Cited by 2,860

Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)

Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., et al. (2018) · 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) · doi:10.1109/isbi.2018.8363547 · Cited by 2,620

Bilinear CNN Models for Fine-Grained Visual Recognition

Lin, T., RoyChowdhury, A., & Maji, S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.170 · Cited by 2,234

Reproducibility of deep learning in digital pathology whole slide image analysis

Fell, C., Mohammadi, M., MORRISON, D. L., Arandjelović, O., Caie, P. D., & Harris‐Birtill, D. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000145 · Cited by 23

Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities

González, R., Saha, A., Campbell, C. J. V., Nejat, P., Lokker, C., & Norgan, A. P. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100347 · Cited by 24

Generalizing to Unseen Domains in Diabetic Retinopathy Classification

Galappaththige, C. J., Kuruppu, G., & Khan, M. H. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00751 · Cited by 20

A Novel Attribute-Based Symmetric Multiple Instance Learning for Histopathological Image Analysis

Vu, T., Lai, P., Raich, R., Pham, A. T., Fern, X. Z., & Rao, U. A. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2987796 · Cited by 20

Developing a Qualification and Verification Strategy for Digital Tissue Image Analysis in Toxicological Pathology

Żuraw, A., Staup, M. A., Klopfleisch, R., Aeffner, F., Brown, D. L., Westerling, T., & Rudmann, D. G. (2020) · Toxicologic Pathology · doi:10.1177/0192623320980310 · Cited by 20

Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images

Marini, N., Otálora, S., Podareanu, D., van Rijthoven, M., van der Laak, J. A. W. M., Ciompi, F., Müller, H., & Atzori, M. (2021) · Frontiers in Computer Science · doi:10.3389/fcomp.2021.684521 · Cited by 23

Digital pathology implementation in cancer diagnostics: towards informed decision-making

Sulaieva, O., Dudin, O., Koshyk, O., Panko, M., & Kobyliak, N. M. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1358305 · Cited by 19

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

Sendra, A. L., Heras, A. D. L., Ávila-Gutiérrez, M. J., & Zamora‐Polo, F. (2020) · Sensors · doi:10.3390/s20061553 · Cited by 20

MORPHIOUS: an unsupervised machine learning workflow to detect the activation of microglia and astrocytes

Silburt, J., & Aubert, I. (2022) · Journal of Neuroinflammation · doi:10.1186/s12974-021-02376-9 · Cited by 21

A glimpse into the future: Integrating artificial intelligence for precision HER2‐positive breast cancer management

Deng, X., Yan, Y., Zhan, Z., Xie, J., Tang, H., Zou, Y., Tu, J., & Liu, P. (2024) · iMetaOmics. · doi:10.1002/imo2.19 · Cited by 20

Generative modeling for renal microanatomy

Murali, L. K., Lutnick, B. R., Ginley, B. G., Tomaszewski, J. E., & Sarder, P. (2020) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2549891 · Cited by 19

Morphometric Cell Classification for Single‐Cell MALDI‐Mass Spectrometry Imaging

Ščupáková, K., Dewez, F., Walch, A. K., Heeren, R. M. A., & Balluff, B. (2020) · Angewandte Chemie · doi:10.1002/ange.202007315 · Cited by 19

Convolutional Neural Networks for Multi-class Histopathology Image Classification.Preprint

Talo, M. (2019) · arXiv (Cornell University) · Cited by 20

Vision Transformers for Low-Quality Histopathological Images: A Case Study on Squamous Cell Carcinoma Margin Classification

Park, S.-Y., Ayana, G., Wako, B. D., Jeong, K. C., Yoon, S.-D., & Choe, S.-W. (2025) · Diagnostics · doi:10.3390/diagnostics15030260 · Cited by 18

The Impact of Artificial Intelligence on Cancer Diagnosis and Treatment: A Review

Najafi, N. N., Hajihassani, H., & Irani, M. A. (2025) · Cancer Informatics · doi:10.1177/11769351251371273 · Cited by 23

AI-driven transformation of precision medicine: a comprehensive narrative review of key application areas, emerging paradigms, and future directions

Zeng, Q., Huang, C., & Zhu, J. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1656603 · Cited by 20

AI Slipping on Tiles: Data Leakage in Digital Pathology

Bussola, N., Marcolini, A., Maggio, V., Jurman, G., & Furlanello, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68763-2_13 · Cited by 18

Hybrid neurofuzzy investigation of short-term variability of wind resource in site suitability analysis: a case study in South Africa

Adedeji, P. A., Akinlabi, S. A., Madushele, N., & Olatunji, O. O. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06001-x · Cited by 19

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

A novel deep CNN model for improved breast cancer detection using ultrasound images

Samanta, P. K., Rout, N. K., & Panda, G. (2023) · International Conference on Communication, Circuits, and Systems (IC3S) · doi:10.1109/ic3s57698.2023.10169383 · Cited by 18

Bio-inspired visual systems based on curved image sensors and synaptic devices

Long, Z., Ding, Y., Poddar, S., Gu, L., Zhang, Q., & Fan, Z. (2023) · Materials Today Electronics · doi:10.1016/j.mtelec.2023.100071 · Cited by 18

Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review

Gonzalez, R., Nejat, P., Saha, A., Campbell, C. J. V., Norgan, A. P., & Lokker, C. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100348 · Cited by 18

A high throughput machine-learning driven analysis of Ca2+ spatio-temporal maps

Leigh, W. A., Valle, G. D., Kamran, S. A., Drumm, B. T., Tavakkoli, A., Sanders, K. M., & Baker, S. A. (2020) · Cell Calcium · doi:10.1016/j.ceca.2020.102260 · Cited by 18

Equine penile squamous cell carcinoma: expression of biomarker proteins and EcPV2

Arthurs, C., Suárez‐Bonnet, A., Willis, C., Xie, B., Machulla, N., Mair, T. S., Cao, K. X., Millar, M., Thrasivoulou, C., Priestnall, S. L., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-64014-3 · Cited by 20

Automated Diabetic Retinopathy Grading using Deep Convolutional Neural NetworkPreprint

Chaturvedi, S. S., Gupta, K., Ninawe, V., & Prasad, P. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.06334 · Cited by 33

Deep-learning application for identifying histological features of epithelial dysplasia of tongue

Nguyen, P.-T.-H., Sakamoto, K., & Ikeda, T. (2022) · Journal of Oral and Maxillofacial Surgery Medicine and Pathology · doi:10.1016/j.ajoms.2021.12.008 · Cited by 17

Artificial intelligence in prostate cancer: Definitions, current research, and future directions

George, R. S., Htoo, A., Cheng, M., Masterson, T. A., Huang, K., Adra, N., Kaimakliotis, H. Z., Akgül, M., & Liang Cheng, C. (2022) · Urologic Oncology Seminars and Original Investigations · doi:10.1016/j.urolonc.2022.03.003 · Cited by 18

Machine Learning Techniques, Detection and Prediction of Glaucoma– A Systematic Review

Mathew, J. C., Ilango, V., & Asha, V. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i5s.6655 · Cited by 17

Generalizability of Deep Learning System for the Pathologic Diagnosis of Various Cancers

Jang, H., Song, I. H., & Lee, S. H. (2021) · Applied Sciences · doi:10.3390/app11020808 · Cited by 19

Multistage classification of oral histopathological images using improved residual network

Panigrahi, S., Bhuyan, R., Kumar, K., Nayak, J., & Swarnkar, T. (2021) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022090 · Cited by 17

Effective active learning in digital pathology: A case study in tumor infiltrating lymphocytes

Meirelles, A. L., Kurc, T., Saltz, J., & Teodoro, G. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106828 · Cited by 17

A semi-supervised learning approach for bladder cancer grading

Wenger, K., Tirdad, K., Cruz, A. D., Mari, A., Basheer, M., Kuk, C., van Rhijn, B. W. G., Zlotta, A. R., van der Kwast, T., & Sadeghian, A. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2022.100347 · Cited by 16

Potential for standardization and automation for pathology and endoscopy in inflammatory bowel disease

Syed, S., & Stidham, R. W. (2020) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izaa211 · Cited by 16

Machine Learning a Predictive Tool for the Analysis of NiCo2S4/Graphene Composites for Supercapacitor

Mulla, H. S., Sawant, D. S., Gaikwad, S. V., Fulari, A. V., Nimat, R. K., Dubal, D. P., & Lohar, G. M. (2025) · ChemSusChem · doi:10.1002/cssc.202402559 · Cited by 15

What Works Where and How for Uptake and Impact of Artificial Intelligence in Pathology: Review of Theories for a Realist Evaluation

King, H., Wright, J. M., Treanor, D. E., Williams, B. J., & Randell, R. (2022) · Journal of Medical Internet Research · doi:10.2196/38039 · Cited by 20

TCNN: A Transformer Convolutional Neural Network for artifact classification in whole slide images

Shakarami, A., Nicolè, L., Terreran, M., Tos, A. P. D., & Ghidoni, S. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104812 · Cited by 19

Healthcare’s new Frontier: AI-driven early cancer detection for improved well-being

Deepa, R., Arunkumar, S., Jayaraj, V., & Sivasamy, A. (2023) · AIP Advances · doi:10.1063/5.0177640 · Cited by 22

Improving diagnosis and outcome prediction of gastric cancer via multimodal learning using whole slide pathological images and gene expression

Xie, Y., Sang, Q., Da, Q., Niu, G., Deng, S., Feng, H., Chen, Y., Li, Y., Liu, B., Yang, Y., et al. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102871 · Cited by 16

DRA-Net: Medical image segmentation based on adaptive feature extraction and region-level information fusion

Huang, Z., Wang, L., & Xu, L. (2024) · Scientific Reports · doi:10.1038/s41598-024-60475-y · Cited by 16

Thorough Analysis of Deep Learning Methods for Diagnosis of COVID-19 CT Images

Gnanaguru, G., Priscila, S. S., Sakthivanitha, M., Radhakrishnan, S., Rajest, S. S., & Singh, S. K. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-5946-4.ch004 · Cited by 16

Adaptive Dynamic Learning Rate Optimization Technique for Colorectal Cancer Diagnosis Based on Histopathological Image Using EfficientNet-B0 Deep Learning Model

El-Ghany, S. A., Mahmood, M. A., & El-Aziz, A. A. A. (2024) · Electronics · doi:10.3390/electronics13163126 · Cited by 16

Evaluation of the impact of deep learning architectural components selection and dataset size on a medical imaging task

Dutta, S., & Gros, E. (2018) · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · doi:10.1117/12.2293395 · Cited by 16

Radiomics at a Glance: A Few Lessons Learned from Learning Approaches

Capobianco, E., & Deng, J. (2020) · Cancers · doi:10.3390/cancers12092453 · Cited by 16

Predictive models for stage and risk classification in head and neck squamous cell carcinoma (HNSCC)

Kumar, S., Patnaik, S., & Dixit, A. (2020) · PeerJ · doi:10.7717/peerj.9656 · Cited by 16

Skin Melanoma Detection in Microscopic Images Using HMM-Based Asymmetric Analysis and Expectation Maximization

Rastghalam, R., Danyali, H., Helfroush, M. S., Celebi, M. E., & Mokhtari, M. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3081185 · Cited by 16

Comparison of texture-based classification and deep learning for plantar soft tissue histology segmentation

Brady, L., Wang, Y.-N., Rombokas, E., & Ledoux, W. R. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104491 · Cited by 15

Empowering digital pathology applications through explainable knowledge extraction tools

Marchesin, S., Giachelle, F., Marini, N., Atzori, M., Boytcheva, S., Buttafuoco, G., Ciompi, F., Nunzio, G. M. D., Fraggetta, F., Irrera, O., et al. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100139 · Cited by 16

MMSRNet: Pathological image super-resolution by multi-task and multi-scale learning

Wu, X., Chen, Z., Peng, C., & Ye, X. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104428 · Cited by 15

SGCL: Spatial guided contrastive learning on whole-slide pathological images

Lin, T., Yu, Z., Xu, Z., Hu, H., Hu, H., & Chen, C. W. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102845 · Cited by 17

Non-annotated renal histopathological image analysis with deep ensemble learning

Koo, J. C., Ke, Q., Hum, Y. C., Goh, C. H., Lai, K. W., Yap, W., & Tee, Y. K. (2023) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-23-46 · Cited by 15

Histopathological Image and Lymphoma Image Classification using customized Deep Learning models and different optimization algorithms

Ganguly, A., Das, R. K. K., & Setua, S. K. (2020) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt49239.2020.9225616 · Cited by 15

Development and operation of a digital platform for sharing pathology image data

Kang, Y., Kim, Y. J., Park, S., Ro, G., Hong, C., Jang, H., Cho, S., Hong, W., Kang, D. U., Chun, J., et al. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01466-1 · Cited by 16

Building robust pathology image analyses with uncertainty quantification

Gomes, J., Kong, J., Kurç, T., de Melo, A. C. M. A., Ferreira, R. C., Saltz, J. H., & Teodoro, G. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106291 · Cited by 16

Highly Efficient Follicular Segmentation in Thyroid Cytopathological Whole Slide Image

Tao, S., Guo, Y., Zhu, C., Chen, H., Zhang, Y., Yang, J., & Liu, J. (2019) · Studies in computational intelligence · doi:10.1007/978-3-030-24409-5_14 · Cited by 18

Joint and individual analysis of breast cancer histologic images and genomic covariates

Carmichael, I. D., Calhoun, B. C., Hoadley, K. A., Troester, M. A., Geradts, J., Couture, H. D., Olsson, L. T., Perou, C. M., Niethammer, M., Hannig, J., et al. (2021) · The Annals of Applied Statistics · doi:10.1214/20-aoas1433 · Cited by 18

Improving Follicular Lymphoma Identification using the Class of Interest for Transfer Learning

Somaratne, U. V., Wong, K. W., Parry, J., Sohel, F. A., Wang, X., & Laga, H. (2019) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta47822.2019.8946075 · Cited by 15

GQ-GCN: Group Quadratic Graph Convolutional Network for Classification of Histopathological Images

Gao, Z., Shi, J., & Wang, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87237-3_12 · Cited by 14

Don't Fear the Artificial Intelligence: A Systematic Review of Machine Learning for Prostate Cancer Detection in Pathology

Frewing, A., Gibson, A. B., Robertson, R., Urie, P. M., & Corte, D. D. (2023) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2022-0460-ra · Cited by 17

A Literature Review of the Future of Oral Medicine and Radiology, Oral Pathology, and Oral Surgery in the Hands of Technology

Singhal, I., Kaur, G., Neefs, D., & Pathak, A. (2023) · Cureus · doi:10.7759/cureus.45804 · Cited by 14

Whole Slide Image Understanding in Pathology: What Is the Salient Scale of Analysis?

Jenkinson, E., & Arandjelović, O. (2024) · BioMedInformatics · doi:10.3390/biomedinformatics4010028 · Cited by 14

The Quest for the Application of Artificial Intelligence to Whole Slide Imaging: Unique Prospective from New Advanced Tools

Faa, G., Castagnola, M., Didaci, L., Coghe, F., Scartozzi, M., Saba, L., & Fraschini, M. (2024) · Algorithms · doi:10.3390/a17060254 · Cited by 17

Ethics of AI in pathology: Current paradigms and emerging issues

Chauhan, C., & Gullapalli, R. R. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95359-7.00009-1 · Cited by 15

Hybrid deep learning and active contour approach for enhanced breast lesion segmentation and classification in mammograms

Nour, A., & Boufama, B. (2025) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2025.100224 · Cited by 15

Evaluation of Colour Pre-processing on Patch-Based Classification of H&E-Stained Images

Bianconi, F., Kather, J. N., & Reyes‐Aldasoro, C. C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-23937-4_7 · Cited by 14

Advances in tissue-based imaging: impact on oncology research and clinical practice

Rahman, A., Jahangir, C. A., Lynch, S. M., Al-Attar, N., Aura, C. M., Russell, N. M., Lanigan, F. T., & Gallagher, W. M. (2020) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2020.1770599 · Cited by 15

Patoloji Görüntülerinin Derin Öğrenme Yöntemleri İle Sınıflandırılması

KARAKURT, M., & İşeri, İ. (2022) · European Journal of Science and Technology · doi:10.31590/ejosat.1011091 · Cited by 16

New fully automatic approach for tissue identification in histopathological examinations using transfer learning

Xu, Y., dos Santos, M. A., Souza, L. F. D. F., Marques, A. G., Zhang, L., Nascimento, J. J. D. C., de Albuquerque, V. H. C., & Filho, P. P. R. (2022) · IET Image Processing · doi:10.1049/ipr2.12449 · Cited by 16

When artificial intelligence meets PD-1/PD-L1 inhibitors: Population screening, response prediction and efficacy evaluation

Jin, W., & Luo, Q. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105499 · Cited by 14

Breast Cancer Classification on Histopathological Images Affected by Data Imbalance Using Active Learning and Deep Convolutional Neural Network

Kwolek, B., Koziarski, M., Bukała, A., Antosz, Z., Olborski, B., Wąsowicz, P., Swadźba, J., & Cyganek, B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30493-5_31 · Cited by 14

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) · RePEc: Research Papers in Economics · Cited by 14

Deep Learning and Its Applications in Computational Pathology

Hong, R., & Fenyö, D. (2022) · BioMedInformatics · doi:10.3390/biomedinformatics2010010 · Cited by 14

Metastatic Breast Cancer Recognition in Histopathology Images Using Convolutional Neural Network with Attention Mechanism

Yu, L., Yang, J., Quan, X., & Zhang, H. (2019) · Chinese Automation Congress · doi:10.1109/cac48633.2019.8997460 · Cited by 14

The Oesophageal Cancer Multidisciplinary Team: Can Machine Learning Assist Decision-Making?

Thavanesan, N., Vigneswaran, G., Bodala, I. P., & Underwood, T. (2023) · Journal of Gastrointestinal Surgery · doi:10.1007/s11605-022-05575-8 · Cited by 13

Federated learning and differential privacy in clinical health: Extensive survey

Odera, D. (2023) · World Journal of Advanced Engineering Technology and Sciences · doi:10.30574/wjaets.2023.8.2.0113 · Cited by 14

Breast Tumor Cellularity Assessment Using Deep Neural Networks

Rakhlin, A., Tiulpin, A., Shvets, A. A., Kalinin, A. A., Iglovikov, V., & Nikolenko, S. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00048 · Cited by 16

Multimodal Latent Semantic Alignment for Automated Prostate Tissue Classification and Retrieval

Lara, J. S., Contreras, V., Otálora, S., Müller, H., & Gonzalez, F. A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59722-1_55 · Cited by 13

Quantitative neurotoxicology: Potential role of artificial intelligence/deep learning approach

Srivastava, A., & Hanig, J. P. (2020) · Journal of Applied Toxicology · doi:10.1002/jat.4098 · Cited by 13

Effects of Image Quantity and Image Source Variation on Machine Learning Histology Differential Diagnosis Models

Betts, E. V., Krause, K., Dubrovsky, A. M. K., Olson, K., Graff, J. P., Mitra, A., Datta-Mitra, A., Beck, K. A., Tsirigos, A., Loomis, C. A., et al. (2021) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_69_20 · Cited by 13

Predicting pathologic complete response in locally advanced rectal cancer patients after neoadjuvant therapy: a machine learning model using XGBoost

Chen, X., Wang, W., Chen, J., Xu, L., He, X., Lan, P., Hu, J., & Lian, L. (2022) · International Journal of Colorectal Disease · doi:10.1007/s00384-022-04157-z · Cited by 12

Content-based histopathological image retrieval using multi-scale and multichannel decoder based LTP

Sukhia, K. N., Riaz, M. M., Ghafoor, A., Ali, S. S., & Iltaf, N. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101582 · Cited by 13

Brain mapping at high resolutions: Challenges and opportunities

Milligan, K., Balwani, A., & Dyer, E. L. (2019) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2019.10.009 · Cited by 13

Determination of the Maturation Status of Dendritic Cells by Applying Pattern Recognition to High-Resolution Images

Lohrer, M. F., Liu, Y., Hanna, D. M., Wang, K.-H., Liu, F., Laurence, T. A., & Liu, G.-Y. (2020) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.0c06437 · Cited by 14

X-ray dark-field phase-contrast imaging: Origins of the concept to practical implementation and applications

Ando, M., Gupta, R., Iwakoshi, A., Kim, J.-K., Shimao, D., Sugiyama, H., Sunaguchi, N., Yuasa, T., & Ichihara, S. (2020) · Physica Medica · doi:10.1016/j.ejmp.2020.11.034 · Cited by 13

The role of artificial intelligence-based foundation models and “copilots” in cancer pathology: potential and challenges

Cheng, H., & Wong, C. C. (2025) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-025-03592-4 · Cited by 16

Survey of Recent Deep Neural Networks with Strong Annotated Supervision in Histopathology

Petríková, D., & Cimrák, I. (2023) · Computation · doi:10.3390/computation11040081 · Cited by 13

A pathology-based diagnosis and prognosis intelligent system for oral squamous cell carcinoma using semi-supervised learning

Zhou, J., Wu, H., Hong, X.-J., Huang, Y., Jia, B., Lu, J., Cheng, B., Xu, M. Q., Yang, M., & Wu, T. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124242 · Cited by 14

GenSelfDiff-HIS: Generative Self-Supervision Using Diffusion for Histopathological Image Segmentation

Purma, V., Srinath, S., Srirangarajan, S., Kakkar, A., & Prathosh, A. P. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3453492 · Cited by 15

Distilling heterogeneous knowledge with aligned biological entities for histological image classification

Wang, K., Zheng, F., Guan, D., Liu, J., & Qin, J. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111173 · Cited by 12

Comprehensive application of artificial intelligence in colorectal cancer: A review

Sun, K., Wang, Y., Qu, R., Yang, Q., Luo, R., Jiang, Z., Wang, H., & Fu, W. (2025) · iScience · doi:10.1016/j.isci.2025.112980 · Cited by 15

Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study

Sikaroudi, M., Safarpoor, A., Ghojogh, B., Shafiei, S., Crowley, M., & Tizhoosh, H. R. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9176279 · Cited by 12

An unsupervised style normalization method for cytopathology images

Chen, X., Yu, J., Cheng, S., Geng, X., Liu, S., Han, W., Hu, J., Chen, L., Liu, X., & Zeng, S. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.06.025 · Cited by 12

Multi-layer perceptron classification & quantification of neuronal survival in hypoxic-ischemic brain image slices using a novel gradient direction, grey level co-occurrence matrix image training

Bhattacharya, S., Bennet, L., Davidson, J. O., & Unsworth, C. P. (2022) · PLoS ONE · doi:10.1371/journal.pone.0278874 · Cited by 12

An improved Hover-net for nuclear segmentation and classification in histopathology images

Wang, J., Qin, L., Chen, D., Wang, J., Han, B., Zhu, Z., & Qiao, G. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08394-3 · Cited by 12

Pathological Cluster Identification by Unsupervised Analysis in 3,822 UK Biobank Cardiac MRIs

Zheng, Q., Delingette, H., Fung, K., Petersen, S. E., & Ayache, N. (2020) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2020.539788 · Cited by 12

Time-frequency time-space long short-term memory networks for image classification of histopathological tissue

Pham, T. D. (2021) · Scientific Reports · doi:10.1038/s41598-021-93160-5 · Cited by 12

A Machine Learning Workflow of Multiplexed Immunofluorescence Images to Interrogate Activator and Tolerogenic Profiles of Conventional Type 1 Dendritic Cells Infiltrating Melanomas of Disease-Free and Metastatic Patients

León-Rodríguez, S. G. D., Hernández‐Herrera, P., Aguilar-Flores, C., Pérez‐Koldenkova, V., Guerrero, A., Mantilla, A., Fuentes‐Pananá, E. M., Wood, C. D., & Bonifaz, L. C. (2022) · Journal of Oncology · doi:10.1155/2022/9775736 · Cited by 12

An automated method for segmentation and quantification of blood vessels in histology images

Bukenya, F., Nerissa, C., Serres, S., Pardon, M., & Bai, L. (2019) · Microvascular Research · doi:10.1016/j.mvr.2019.103928 · Cited by 12

Accurate Diagnosis of Colorectal Cancer Based on Histopathology Images Using Artificial IntelligencePreprint

Wang, K.-S., Yu, G., Xu, C., Meng, X., Zhou, J., Zheng, C., Deng, Z., Shang, L., Liu, R., Su, S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.15.992917 · Cited by 12

Graph Convolutional Neural Networks to Classify Whole Slide Images

Konda, R., Wu, H., & Wang, M. D. (2020) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac48688.2020.0-169 · Cited by 11

Which data subset should be augmented for deep learning? a simulation study using urothelial cell carcinoma histopathology images

Ameen, Y. A., Badary, D. M., Abonnoor, A. E. I., Hussain, K. F., & Sewisy, A. A. E.-M. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05199-y · Cited by 14

Analysis of histopathological images using machine learning – A survey

Saloni, Kumar, S., & Vijh, S. (2025) · CRC Press eBooks · doi:10.1201/9781003532521-59

Machine learning in computational histopathology: Challenges and opportunities

Cooper, M. C., Ji, Z., & Krishnan, R. G. (2023) · Genes Chromosomes and Cancer · doi:10.1002/gcc.23177 · Cited by 87

Machine learning methods for histopathological image analysis: updates in 2024

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

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

Machine learning approaches for pathologic diagnosis

Komura, D., & Ishikawa, S. (2019) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-019-02594-w · Cited by 143