Cited by 203AI in cancer detectionArtificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.

Artificial intelligence in digital breast pathology: Techniques and applications

Open access

Ibrahim, Asmaa, Gamble, Paul, Jaroensri, Ronnachai, Abdelsamea, Mohammed M., Mermel, Craig H., Chen, Po-Hsuan Cameron, Rakha, Emad A.·2020·The Breast

DOI 10.1016/j.breast.2019.12.007PMC PMC7375550

Open this paper in CoChat — free account Open PDF

Abstract

Breast cancer is the most common cancer and second leading cause of cancer-related death worldwide. The mainstay of breast cancer workup is histopathological diagnosis - which guides therapy and prognosis. However, emerging knowledge about the complex nature of cancer and the availability of tailored therapies have exposed opportunities for improvements in diagnostic precision. In parallel, advances in artificial intelligence (AI) along with the growing digitization of pathology slides for the primary diagnosis are a promising approach to meet the demand for more accurate detection, classification and prediction of behaviour of breast tumours. In this article, we cover the current and prospective uses of AI in digital pathology for breast cancer, review the basics of digital pathology and AI, and outline outstanding challenges in the field.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1016/j.breast.2019.12.007

Cites

81 of 81 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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 · cited 1×
“Deep learning techniques utilize millions of neuron-like units in order to learn complex relationship between image pixel values and its semantic labels, without the need for manual feature engineering–the features are learned automatically from data [20,21].”
Background
Deep learning
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066 · cited 1×
“Most ML algorithms can be viewed as mathematical models that map a set of observed variables, known as ‘ features ’ or ‘ predictors ’ , of a data point or sample, into a set of outcome variables, known as ‘ labels ’ or ‘ targets ’ [18,19].”
Background
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248 · cited 1×
“Previous study [35] also explored interpretability methods [71,72] to understand what were the input features that triggers the model’s activation in identifying tumor.”
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697
Neural networks: A comprehensive foundation
Tadeusiewicz, R. (1995) · Control Engineering Practice · doi:10.1016/0967-0661(95)90080-2 · Cited by 7,217
Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293 · cited 2×
“Babaketal.,2017 detected lymph node metastasis in breast cancer patients with a higher diagnostic achievement over 11 pathologists [33]”
BackgroundMethod
Pattern recognition and machine learning
(2007) · Choice Reviews Online · doi:10.5860/choice.44-5091 · Cited by 2,783

Flagged first, then by citations

Show all 81 references

Research works printed but not linked: 3

Mukhopadhyay, 2017, Whole slide imaging versus microscopy for primary diagnosis in surgical pathology, Am J Surg Pathol, 1
2015, Website. the spatial heterogeneity and molecular correlates of lymphocytic infiltration in triple-negative breast cancer, J R Soc Interface
Basavanhally, 2010, Computerized image-based detection and grading of lymphocytic infiltration in HER2 breast cancer histopathology, IEEE (Inst Electr Electron Eng) Trans Biomed Eng, 57, 642

Sorted by citations

Items 101–200 of 203

Artificial Intelligence in the Clinical Laboratory
Baron, J. M. (2022) · Clinics in Laboratory Medicine · doi:10.1016/j.cll.2022.09.002 · Cited by 6
Image‐Based Breast Cancer Histopathology Classification and Diagnosis Using Deep Learning Approaches
Aldakhil, L. A., Alhasson, H. F., Alharbi, S. S., Khan, R. U., & Qamar, A. M. (2025) · Applied Computational Intelligence and Soft Computing · doi:10.1155/acis/7011984 · Cited by 7
An interpretable deep learning model for detecting BRCA pathogenic variants of breast cancer from hematoxylin and eosin-stained pathological images
Li, Y., Xiong, X., Liu, X., Wu, Y., Li, X., Liu, B., Lin, B., Yu, L., & Xu, B. (2024) · PeerJ · doi:10.7717/peerj.18098 · Cited by 7
Recent advances in porous organic framework-based aptasensors for diagnosis of cancer diseases
Azzouz, A., Hejji, L., & Kim, K. (2024) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2024.118106 · Cited by 6
An Approach toward Automatic Specifics Diagnosis of Breast Cancer Based on an Immunohistochemical Image
Berezsky, O., Pitsun, O., Melnyk, G., Datsko, T., Izonin, I., & Derysh, B. (2023) · Journal of Imaging · doi:10.3390/jimaging9010012 · Cited by 6
“The relevance of breast cancer research and the use of AI for these tasks are explored in the research study [1].”
Background
Mutational signatures for breast cancer diagnosis using artificial intelligence
Odhiambo, P., Okello, H., Wakaanya, A., Wekesa, C., & OKOTH, P. K. (2023) · Journal of the Egyptian National Cancer Institute · doi:10.1186/s43046-023-00173-4 · Cited by 7
“Traditionally, breast cancer diagnosis and treatment have been based on clinical symptoms, tumor shape, and site of origin [3, 4].”
Background
Differentiation Between Phyllodes Tumors and Fibroadenomas of Breast Using Mammography-based Machine Learning Methods: A Preliminary Study
Deng, X., Cao, P., Nan, S., Pan, Y.-P., Yu, C., Pan, T., & Dai, G. (2023) · Clinical Breast Cancer · doi:10.1016/j.clbc.2023.07.002 · Cited by 7
Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise
McDonnell, K. J. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12144830 · Cited by 7
Enhanced metastasis risk prediction in cutaneous squamous cell carcinoma using deep learning and computational histopathology
Peleva, E., Chen, Y., Finke, B., Rizvi, H., Healy, E., Lai, C., Craig, P. J., Rickaby, W., Schoenherr, C., Nourse, C., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-01065-7 · Cited by 5
PatchSight–ImmuneMap–LifeSpan as a unified AI framework for breast cancer diagnosis, immune profiling and prognostic prediction
Al-Nussairi, A. K. J., Ali, A. B. M., Malik, S., Patro, S. G. K., Mahanty, C., Abass, K. S., Basheti, I. A., Dessalegn, A. A., Muzammil, K., & Kumar, S. (2026) · Discover Oncology · doi:10.1007/s12672-026-04491-6 · Cited by 5
“Few studies have counted and estimated immu-noscore from lymphocytes [54, 55].”
Algorithm-assisted diagnosis of Hirschsprung’s disease – evaluation of robustness and comparative image analysis on data from various labs and slide scanners
Greenberg, A., Samueli, B., Farkash, S., Zohar, Y., Ish-Shalom, S., Hagege, R. R., & Hershkovitz, D. (2024) · Diagnostic Pathology · doi:10.1186/s13000-024-01452-x · Cited by 5
Comparison of glass and digital slides for cervical cytopathology screening and interpretation
Velozo, G. D. S., Cordeiro, J., Sousa, J., Holanda, A. C., Pessoa, G., Porfírio, M. M. A. E., & Tavora, F. R. F. (2023) · Diagnostic Cytopathology · doi:10.1002/dc.25209 · Cited by 5
“21,25,26 We used the standard 20 (cid:1) resolution for this study, and based on our results, a higher magnification would likely be needed to assess the cytological flora.”
BackgroundMethod
End-to-End Deep Learning for Detecting Metastatic Breast Cancer in Axillary Lymph Node from Digital Pathology Images
Turki, T., Al-Sharif, A., & Taguchi, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-91608-4_34 · Cited by 5
ARTIFICIAL INTELLIGENCE AND NEXT GENERATION PATHOLOGY: TOWARDS PERSONALIZED MEDICINE
Dudin, O., Mintser, O. P., & Sulaieva, O. (2021) · Proceedings of the Shevchenko Scientific Society Medical Sciences · doi:10.25040/ntsh2021.02.07 · Cited by 6
DHHoE : Deep hybrid homogenous ensemble for digital histological breast cancer classification
Zerouaoui, H., Idri, A., & Alaoui, O. E. (2023) · Expert Systems · doi:10.1111/exsy.13397 · Cited by 4
“…digital images using whole slide scanners, results in lower inter-pathologist variation in diagnostic decisions (Hameed et al., 2020), as well as more objective decisions (Ibrahim et al., 2020) particularly when combining the advantages of both machine learning (ML) and deep learning (DL) models.”
Advancing Content-Based Histopathological Image Retrieval Pre-Processing: A Comparative Analysis of the Effects of Color Normalization Techniques
Tabatabaei, Z., Pérez-Bueno, F., Colomer, A., Moll, J. O., Molina, R., & Naranjo, V. (2024) · Applied Sciences · doi:10.3390/app14052063 · Cited by 4
Combined proliferation and apoptosis index provides better risk stratification in breast cancer
Ibrahim, A., Toss, M. S., Atallah, N. M., Saleem, M. A., Green, A. R., & Rakha, E. A. (2023) · Histopathology · doi:10.1111/his.14887 · Cited by 4
“The morphological assessment approach adopted in this study agrees with previous trials on a variety of tumour types, and this could be improved further through the introduction of automated or artificially assisted algorithms, as the implementation of such algorithms for automated quantification of both apoptosis and…”
Method
Artificial Intelligence in Histologic Diagnosis of Ductal Carcinoma In Situ
Co, M. T.-H., Lau, Y. C. C., Qian, Y. X. Y., Chan, M. C. R., Wong, D. K.-K., Lui, K. H., So, N. Y. H., Tso, S. W. S., Lo, Y. C., Lee, W. J., et al. (2023) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2023.05.008 · Cited by 4
Intelligent recognition of shale fracture network images based on transfer learning
Wang, Q., Hu, J., Liu, P., & Sun, G. (2023) · Earth Science Informatics · doi:10.1007/s12145-023-01202-5 · Cited by 4
“…computer vision (Voulodimos et al. 2018), speech recognition (Song 2020) as well as diverse engineering disciplines (Liu et al. 2018), including tunneling (He et al. 2019), road and bridge engineering (Yang et al. 2021; Jin et al. 2006), and geotechnical engineering (Ibrahim et al. 2020).”
Artificial Intelligence for the Identification of Biomarkers in Cancer Prevention and Diagnosis: Advances and Perspectives
Barioni, C. T. S., Wandresen, R. P. D. B., Pereira, L. F., Coimbra, A. F., Kubo, B. B. D. A. O., & da Cunha, R. C. (2024) · Revista Brasileira de Cancerologia · doi:10.32635/2176-9745.rbc.2024v70n2.4692 · Cited by 4
“For example, the GGR ratio was identified as a positive prognostic marker for breast cancer, while other biomarkers such as HER2, ER, PR, and Ki-67 were used to predict risk categories and therapy efficacy 3,5,22,24 .”
BackgroundMethodInfluential
A Comparative Study Assessing the Confidence of Doctors in Conventional vs Artificial Intelligence Assisted Radiological Diagnoses for Patient Care
Puzhakkal, I. F., Shah, S., Nair, S., Patel, P., OrthoGlobe, U. K., George, A., Adya, V., Koumbem, A. M. A., & Kanhangad, D. (2024) · Journal of Emergency Medicine Open Access · doi:10.33140/jemoa.02.01.14 · Cited by 4
Digital pathology as a platform for primary diagnosis and augmentation via deep learning
Parwani, A. V. (2020) · Elsevier eBooks · doi:10.1016/b978-0-323-67538-3.00006-3 · Cited by 4
Deep Fisher Vector Coding For Whole Slide Image Classification
Akbarnejad, A., Ray, N., & Bigras, G. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433836 · Cited by 4
“, their pooling strategies, their modeling assumptions, or the challenges that they address [2, 6].”
Background
A survey on deep learning approaches for breast cancer diagnosisPreprint
Kwong, T., & Mazaheri, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.08853 · Cited by 8
“Images from breast cancer screenings with poor resolution, sharpness, contrast, focus, or high noise can hinder the model during training, predictions, or localization of lesions [17].”
Background
WITHDRAWN: Artificial Intelligence (AI) in Breast Cancer Care - Leveraging multidisciplinary skills to improve care
Cardoso, M. J., Ferrera, C., Román, J. A. S., Houssami, N., Pozzi, G., & Brigitte Séroussi, A. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.102000 · Cited by 3
Retracted
Evaluation of breast tumor morphologies from African American and Caucasian patients
Stone, A., Kalahiki, C. B., Li, L., Hubig, N., Iuricich, F., & Dunn, H. W. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.06.019 · Cited by 3
“Tissue and cell level features can be identified using morphological structures which allows the computer program to discriminate between different histological components [44].”
Background
Machine learning in medicine: Medical droids, tricorders, and a computer named Hal 9000
Bajema, I. M. (2021) · Néphrologie & Thérapeutique · doi:10.1016/j.nephro.2020.03.002 · Cited by 3
Artificial intelligence as an adjunct tool for breast oncologists – are we there yet?
Schlam, I. M., Menezes, M. C. S., Corti, C., Tan, A., Abuali, I. A., & Tolaney, S. M. (2023) · ESMO Open · doi:10.1016/j.esmoop.2023.101643 · Cited by 3
“AI can also have an impact within pathology, which is critical to establish a breast cancer diagnosis and to determine the optimal treatment for an individual patient.(3) In one study, the inter-observer diagnostic concordance for breast biopsy diagnoses among pathologists was only 75%, with the most variability noted…”
Background
Transfer learning drives automatic HER2 scoring on HE-stained WSIs for breast cancer: a multi-cohort study
Li, X., Lin, Z., Qiu, C., Zhang, Y., Lei, C., Shen, S., Zhang, W., Lai, C., Li, W., Huang, H., et al. (2025) · Breast Cancer Research · doi:10.1186/s13058-025-02008-7 · Cited by 2
“To address this challenge, machine learning was introduced to enhance the consistency of HER2 identification on IHC-stained whole slide images (WSIs) [7, 8].”
ICAM: An interpretable auxiliary model for the pathological diagnosis of breast cancer based on knowledge embedding
Gu, L., Xiong, X., Liu, Q., Lei, D., Wang, R., Bo, L., Wang, G., & Xu, B. (2025) · Information Sciences · doi:10.1016/j.ins.2025.122521 · Cited by 2
Inteligencia artificial en la detección del cáncer de mama por tomosíntesis, ¿hacia dónde vamos? Revisión Narrativa
Freitas, M. A. F. D., & Capecchi, A. (2022) · Revista Científica CMDLT · doi:10.55361/cmdlt.v15i2.66 · Cited by 2
Use of Artificial Intelligence in Implementing Mainstream Precision Medicine to Improve Traditional Symptom-driven Practice of Medicine: Allowing Early Interventions and Tailoring better-personalised Cancer Treatments
Luvhengo, T. E., Molefi, T., Demetriou, D., Hull, R., & Dlamini, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-21506-3_3 · Cited by 3
Comparative Analysis of Nucleus Segmentation Techniques for Enhanced DNA Quantification in Propidium Iodide-Stained Samples
Jónás, V. Z., Paulik, R., Molnar, B. A., & Kozlovszky, M. (2024) · Applied Sciences · doi:10.3390/app14198707 · Cited by 2
The Potential Diagnostic Application of Artificial Intelligence in Breast Cancer
Behzadi, M., Azinfar, A., Alshakarchi, H. I., Khazaei, Y., Gataa, I. S., Ferns, G. A., Naderi, H., Avan, A., Fiuji, H., & Rad, M. P. (2025) · Current Pharmaceutical Design · doi:10.2174/0113816128369168250311172823 · Cited by 2
The Role of Artificial Intelligence for Image Analysis in Surgical Pathology
Bălţătescu, G. I., Aşchie, M., Deacu, M., Petcu, L. C., Dobrin, N., Nicolau, A.-A., Chisoi, A., Burlacu, I., Iordache, I. E., & Steriu, L. (2020) · Annals of Dunarea de Jos University of Galati Fascicle I Economics and Applied Informatics · doi:10.35219/eai15840409104 · Cited by 2
Radiology and Radiomics: Towards Oncology Prediction with IA and Big Data
Ayres, A. S., Ferraciolli, S. F., Mota, A. L., Polsin, L. L. M., & Leite, C. D. C. (2022) · Integrated science · doi:10.1007/978-3-031-11199-0_12 · Cited by 2
Histological breast cancer classification using CNN and MLP based ensembles
Zerouaoui, H., Idri, A., & Alaoui, O. E. (2022) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa56895.2022.10017667 · Cited by 2
“…into digital images using whole slide scanners, results on lowering the inter-pathologist variation in diagnostic decision [4], and provides a more objective decisions [5] especially when combined with the machine learning (ML) classifiers and deep learning (DL) architectures for classification.”
Background
Development of high‐quality artificial intelligence for computer‐aided diagnosis in determining subtypes of colorectal cancer
Weng, W., Yoshida, N., Morinaga, Y., Sugino, S., Tomita, Y., Kobayashi, R., Inoue, K., Hirose, R., Dohi, O., Itoh, Y., et al. (2024) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.16661 · Cited by 2
Deep Learning Technology in Pathological Image Analysis of Breast Tissue
Liu, Y., Wang, X., Li, J., Hao, L., Zhao, T., Zou, H., & Xu, D. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/9610830 · Cited by 2
“With the rapid development of computer technology, automatic analysis of digital pathological images becomes possible [11].”
Background
Retracted
Clinical and pathological diagnosis using AI and data-driven science
Hiraoka, S.-I. (2020) · Journal of Japanese Society of Oral Oncology · doi:10.5843/jsot.32.159 · Cited by 1
Recent Advances in Intraoperative Lumpectomy Margin Assessment for Breast Cancer
Luo, T., Lu, T., Yu, B., & Yen, T. W. F. (2022) · Current Breast Cancer Reports · doi:10.1007/s12609-022-00451-5 · Cited by 1
StainSegNet: Stain Normalization and Segmentation-Driven Colorectal Tumor Tissues Classification
Ponnarasee, B. K., Lalithamani, N., & Ajibesin, A. A. (2023) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci62032.2023.00196 · Cited by 1
“This study sets off on an important journey, attempting to improve the accuracy and efficiency of CRC detection with a complex strategy that incorporates stain normalization and segmentation-driven tissue classification [4][5].”
Background
Comparison of Digital Pathology and Light Microscopy Among Dermatology Residents: A Reappraisal Following Practice Changes
Neal, D. E., Johnson, E. F., Agrawal, S., Todd, A. W., Camilleri, M., & Wieland, C. N. (2024) · American Journal of Dermatopathology · doi:10.1097/dad.0000000000002805 · Cited by 1
Development and validation of a HER2 scoring model using HE-stained WSIs across multiple cohorts
Lin, Z., Fan, M., Li, X., Huang, H., & Li, L. (2025) · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · doi:10.1117/12.3045920 · Cited by 1
“To address this challenge, machine learning has been introduced to enhance the consistency of HER2 assessment on IHC-stained whole slide images (WSIs) [5] .”
Classifying breast intraductal proliferative lesions via a knowledge distillation framework using convolutional neural network-based nuclei-segmentation-assisted classification (KDCNN-NSAC)
Li, X., Chen, J., Luo, B., Xia, M., Zhang, X., Zhu, H., Zhang-Cai, Y., Chen, Y., Yang, Y., & Wen, Y. (2025) · Heliyon · doi:10.1016/j.heliyon.2025.e42756 · Cited by 2
Classification of Ovarian Cancer Subtype Using Histopathology Images
Parvathi, R., & Yuan, X. (2025) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3693-3840-7.ch003 · Cited by 1
Enhanced nuclear information fusion and visual transformer for pathological breast cancer image classification
Zhang, Q., Gao, H., Li, W., Xu, Z., Ouyang, T., & Gu, Z. (2025) · Scientific Reports · doi:10.1038/s41598-025-04344-2 · Cited by 2
“Among these, the convolutional neural network (CNN) has played an important role in medical image analysis, particularly in detection, classification, and segmentation tasks 5,6 .”
A Pilot Study of Breast Cancer Histopathological Image Classification Using Google Teachable Machine: A No-Code Artificial Intelligence Approach
Singla, N., Ghosh, A., Dhingra, M., Pal, U., Dasgupta, A., Ghosh, A., Kuc, T., & Singla, K. (2025) · Cureus · doi:10.7759/cureus.87301 · Cited by 1
“Democratizing AI development through no-code solutions may facilitate broader deployment of AI-assisted diagnostic tools in regions that face workforce shortages and increasing diagnostic demands [11].”
Validation of a deep learning-based AI model for breast cancer risk stratification in postmenopausal ER+/HER2-breast cancer patients
Pouplier, S. S., Sharma, A., Ruusuvuori, P., Hartman, J., Jensen, M., Ejlertsen, B., Rantalainen, M., & Lænkholm, A. (2025) · The Breast · doi:10.1016/j.breast.2025.104671 · Cited by 1
“Deep learning-based AI-models show great potential to enhance accuracy and efficiency in pathology [9,10].”
Distinction Between Benign and Borderline/Malignant Phyllodes Tumor in Breast Mammography and Ultrasound Based on Radiomics Methods
Su, X., Li, C., Chen, J., Cui, C., Bian, T., Li, L., Sun, N., & Wang, Q. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.01.045 · Cited by 1
Petrographic thin section image classification using traditional and deep learning techniques − A comparative evaluation
Sinha, P. P., Ramu, M., & Srinivasan, V. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.114898 · Cited by 1
Breast pathology update
Teo, K., & Mallon, E. (2021) · Surgery (Oxford) · doi:10.1016/j.mpsur.2021.11.013 · Cited by 1
High-impact opportunities to address ischemia: a focus on heart and circulatory research
Rimon, R. A., Nelson, V. L., Brunt, K. R., & Kassiri, Z. (2022) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00402.2022 · Cited by 1
The Application of AI in Precision Oncology: Tailoring Diagnosis, Treatment, and the Monitoring of Disease Progression to the Patient
Dlamini, Z., & Hull, R. (2023) · Springer eBooks · doi:10.1007/978-3-031-21506-3_1 · Cited by 2
Use of artificial intelligence to predict response to neoadjuvant chemotherapy in breast cancer
Goulart, K. O. B., Kneubil, M. C., Brollo, J., Orlandin, B. C., Corso, L. L., Roesch‐Ely, M., & Henriques, J. A. P. (2023) · Mastology · doi:10.29289/2594539420220041 · Cited by 1
Comparative Assessment of Deep End-To-End, Deep Hybrid and Deep Ensemble Learning Architectures for Breast Cancer Histological Classification
Zerouaoui, H., & Idri, A. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-43471-6_5 · Cited by 1
Machine Learning-Based Algorithms for Breast Cancer Prediction
Abdallah, C. B., & Moh, A. N. S. (2023) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc58704.2023.10319051 · Cited by 1
Efficient transfer learning techniques for breast cancer histopathological image classification
Nayagam, R. D., & Selvathi, D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13999-4.00010-9 · 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
The Present State and Potential Applications of Artificial Intelligence in Cancer Diagnosis and Treatment
Mishra, A., Sharma, S., & Pandey, S. K. (2025) · Recent Patents on Anti-Cancer Drug Discovery · doi:10.2174/0115748928361472250123105507 · Cited by 3
Refugee Issues in the 21st Century: Challenges to Humanity and World Diplomacy
Basir, S. M., & Aziz, S. N. A. (2019) · Jurnal Undang-Undang dan Masyarakat · doi:10.17576/juum-2019-25-07 · Cited by 1
ExplainEx: An Explainable Artificial Intelligence Framework for Interpreting Predictive Models
Udenwagu, N. E., Azeta, A. A., Misra, S., Nwaocha, V. O., Enosegbe, D. L., & Sharma, M. M. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-73050-5_51 · Cited by 1
Applications and implications of whole-slide imaging in breast pathology
Millican-Slater, R. A., & Rakha, E. A. (2021) · Diagnostic histopathology · doi:10.1016/j.mpdhp.2021.12.003 · Cited by 1
Imaginando el futuro
Bernet, L., Piñero, A., Pérez, S., Vidal‐Vanaclocha, F., Zotano, Á. G., Vidal‐Sicart, S., Sanz, J., Gago, B., & Menjón, S. (2022) · Revista de Senología y Patología Mamaria · doi:10.1016/j.senol.2022.06.001
Mayfly-Algorithm Selected Features for Classification of Breast Histology Images into Benign/Malignant Class
Kadry, S., Rajinikanth, V., Srivastava, G., & Meqdad, M. N. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21517-9_6
A Genome-Informed Functional Modeling Approach to Evaluate the Responses of Breast Cancer Patients to CDK4/6 Inhibitors-Based Therapies and Simulate Real-World Clinical TrialsPreprint
Yang, M., Liu, Y., Zhang, C., Hsueh, Y.-C., Zhang, Q., Fan, Y., Xu, J., Huang, M., Li, X., Yang, J., et al. (2023) · medRxiv · doi:10.1101/2023.05.15.23289976
“As artificial intelligence (AI) methods continue to advance in biomedical research, they offer great potential for exploring the mechanisms of drug response 20,21 .”
Background
Visualizing the Invisible: Microscopy and How It Affects Our Understanding of Cells and Tissues
Hutmacher, F., Opstad, I. S., Hutmacher, F., & Ströhl, F. (2023) · Biomedical visualization · doi:10.1007/978-3-031-36850-9_1
Commentary on “Comparison of glass and digital slides for cervical cytopathology screening and interpretation”
Goel, A., Kapatia, G., Parwaiz, A., & Gupta, S. (2023) · Diagnostic Cytopathology · doi:10.1002/dc.25242
“5 While the study reported a nice diagnostic parallel between digital and optical microscopy, particularly about cellular atypia and inflammatory processes, it faced notable challenges in microbiology diagnoses.”
Background
Application of Convolutional Neural Network in Ultrasound Diagnosis of Lymph Nodes
伍, 杰. (2023) · Advances in Clinical Medicine · doi:10.12677/acm.2023.13102287
Basic Principles of Pathology
Yanoff, M., & Sassani, J. W. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-87822-7.00001-8
Patologia digital e computacional: um panorama atual e perspectivas futuras
Dalmedico, M. M., de Carvalho, C. K. L., de Oliveira, P. P., Hembecker, P. K., Ávila, J. L. S., & Ioshii, S. O. (2024) · Contribuciones a las Ciencias Sociales · doi:10.55905/revconv.17n.4-103 · Cited by 1
Digital dermatopathology implementation: Experience at a multisite academic institution
Proffer, S. L., Reinhart, J. P., Ridgeway, J. L., Barry, B. A., Kamath, C. C., Gerdes, E. O. W., Todd, A., Cervenka, D. J., DiCaudo, D. J., Sokumbi, O., et al. (2024) · Journal of Cutaneous Pathology · doi:10.1111/cup.14629
“…also allow for improved diagnostic accuracy and reduced error rates, as pathologists can easily access and analyze large data sets in a shared location, 25,26 and efficiently synthesize information necessary for medical decision making, enabling better-informed, thus likely more accurate diagnoses.”
Generalizing AI-Driven Assessment of Immunohistochemistry Across Immunostains and Cancer Types: A Universal Mmunohistochemistry AnalyzerPreprint
Brattoli, B., Mostafavi, M., Lee, T., Jung, W., Ryu, J., Park, S., Park, J., Pereira, S., Shin, S.-H., Choi, S., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4857105
Expanding role and scope of artificial intelligence in the field of gastrointestinal pathology
Mubarak, M., Rashid, R., Sapna, F., & Shakeel, S. (2024) · Artificial Intelligence in Gastroenterology · doi:10.35712/aig.v5.i2.91550
Detecting cell types and densities in the tumor microenvironment improves prognostic risk assessment for breast cancer
Liu, P., Zhang, X., Wang, W., Zhu, Y., Xie, Y., Tai, Y., & Ma, J. (2024) · Biomolecules and Biomedicine · doi:10.17305/bb.2024.10974
Breast pathology update
Teo, K., & Mallon, E. (2024) · Surgery (Oxford) · doi:10.1016/j.mpsur.2024.09.009
THE NORMAL BONE MARROW
Barbara J. Bain MBBS, F., David M. Clark MD, M. (., Bridget S. Wilkins DM, P., & Vishakha Sovani MBBS, M. (2024) · doi:10.1002/9781394244843.ch1
Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images
Gupta, R. K., Das, S., & Sethi, A. (2024) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe63649.2024.10820477
Monitoring the Examination of Digitised Pathology Samples in a 3D VR Environment
Vincze, M., Burian, S., & Kozlovszky, M. (2024) · IEEE International Symposium on Logistics and Industrial Informatics (LINDI) · doi:10.1109/lindi63813.2024.10820419
Efficient Whole Slide Image Classification Through Fisher Vector Representation
Gupta, R. K., Dharani, D., Shanker, S., & Sethi, A. (2024) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe63649.2024.10820480
“Typically, classification methods for whole slide images (WSI) are grouped based on various factors such as their pooling techniques, the assumptions underlying their models, or the specific challenges they aim to tackle [5], [6].”
The role of Artificial Intelligence in detecting breast lesions using ultrasound
Ziemińska, D., Motolko, K., Burczyk, R., Duszyński, K., Tokarczyk, E., Michalska, M., & Łabuda, A. (2025) · Quality in Sport · doi:10.12775/qs.2025.37.57301
Systematic Review of the Effectiveness of Mammography and Alternative Screening Methods in Breast Cancer Programs
Anbazhagan, S., Saraswat, V., Mohapatra, S. S. G., Joginder, Jamuna., K. V., Kanchana, A., & Kolaventi, S. S. (2024) · Seminars in Medical Writing and Education · doi:10.56294/mw2024533
Breast Pathology Through the Digital Lens
Heller, A., Kowtha, L., Tiwari, K., & Harigopal, M. (2025) · Seminars in Diagnostic Pathology · doi:10.1016/j.semdp.2025.150914
An Information-Extreme Algorithm for Universal Nuclear Feature-Driven Automated Classification of Breast Cancer Cells
Savchenko, T., Lakhtaryna, R., Denysenko, A., Dovbysh, A. S., Coupland, S. E., & Moskalenko, R. (2025) · Diagnostics · doi:10.3390/diagnostics15111389
“Using deep learning (DL) and image recognition algorithms significantly improves the accuracy and speed of diagnosis [10].”
Uso de la inteligencia artificial en el diagnóstico de cáncer de mama
Montero-Cadena, K. D., & Silva-Acosta, J. D. C. (2025) · Ciencia Latina Revista Científica Multidisciplinar · doi:10.37811/cl_rcm.v9i3.17982
Artificial Intelligence in Early Diagnosis of Breast Cancer
Sapna, Garg, V. K., & Asmi (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-7289-9_15
Artificial Intelligence Models for the Detection of Breast Cancer in Medical Imaging
Akaydın, E., Arslan, A. R., & Çiçek, G. (2025) · Medical Technologies Congress (TIPTEKNO) · doi:10.1109/tiptekno68206.2025.11270140
“[2].”
Additional file 2 of Value assessment of artificial intelligence in medical imaging: a scoping review
Fasterholdt, I., Naghavi‐Behzad, M., Rasmussen, B. S. B., Kjølhede, T., Skjøth, M. M., Hildebrandt, M. G., & Kidholm, K. (2022) · Figshare · doi:10.6084/m9.figshare.21443630
Additional file 1 of Value assessment of artificial intelligence in medical imaging: a scoping review
Fasterholdt, I., Naghavi‐Behzad, M., Rasmussen, B. S. B., Kjølhede, T., Skjøth, M. M., Hildebrandt, M. G., & Kidholm, K. (2022) · Figshare · doi:10.6084/m9.figshare.21443627
Additional file 1 of Algorithm-assisted diagnosis of Hirschsprung’s disease – evaluation of robustness and comparative image analysis on data from various labs and slide scanners
Greenberg, A., Samueli, B., Farkash, S., Zohar, Y., Ish-Shalom, S., Hagege, R. R., & Hershkovitz, D. (2024) · Figshare · doi:10.6084/m9.figshare.26677619
1Chapter 1 Artificial intelligence in cancer treatment and management
Malviya, R., Rajput, S., Roy, M., Sridhar, S. B., & Thomas, S. (2025) · De Gruyter eBooks · doi:10.1515/9783111670744-001
The Impact of Artificial Intelligence on Cancer Biomarker Detection and Analysis
Nusratullah, S., Anjankar, A. P., Dhok, A., Basheeruddin, M., Ingole, R. P., & Batulwar, P. S. (2025) · DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIHEI) · doi:10.1109/idicaihei65991.2025.11379136
“[16][17].”
Random optimization-based feature selection for breast cancer outcome prediction using fully connected neural networks on multi-omic data
Tamrakar, P., & Pathak, G. R. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01205-3
“Nonetheless, there is a fresh perception of the complexity of cancer [17].”
Lipid Metabolism-Related Rapid Pathological Diagnosis and Staging of Breast Tissue Sections Using Two-Photon Fluorescence Imaging Assisted with a Carbonized Polymer Dot Probe
Yu, Z., Wang, J., Liu, Y., Xu, W., Han, B., & Xu, S. (2026) · Chemical & Biomedical Imaging · doi:10.1021/cbmi.6c00025
THE ROLE OF DEEP LEARNING IN THE DIAGNOSIS OF BREAST TUMORS BASED ON WHOLE SLIDE IMAGING DATA
Starchenko, I. I., Prylutskyi, О. К., Fylenko, B. M., Ройко, Н. В., & Черняк, В. В. (2026) · Актуальні проблеми сучасної медицини Вісник Української медичної стоматологічної академії · doi:10.31718/2077-1096.26.1.316
A Review on the Logistics, Financial, Ethical, and Regulatory Frameworks of Artificial Intelligence in Digital Pathology
Tang, Y. L., & Dahlmeier, D. (2026) · Apmis · doi:10.1111/apm.70227
Development and evaluation of docetaxel-hesperidin loaded nanoformulation for the treatment of breast cancer
Raja, M., Shah, S., & Yadav, B. K. (2026) · Journal of Biomaterials Science Polymer Edition · doi:10.1080/09205063.2026.2690438
Artificial Intelligence and Digital Pathology for Preoperative Lymphovascular Invasion and Metastatic Risk Prediction in Breast Cancer: Bridging the Biopsy–Resection Gap
Alshreef, B. S., & Kariri, Y. A. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15145381
AI-Driven Innovations in Cancer Care: A Comprehensive Overview
Li, J., Yeang, H. A., & Perera, R. (2026) · Springer eBooks · doi:10.1007/978-3-032-11023-7_6
Artificial intelligence revolution in breast cancer management: a comprehensive review
Shang, Y., Chen, T., Li, X., Li, J., & Yang, Q. (2026) · Molecular and Digital Medicine · doi:10.1016/j.mdmed.2026.100010

Authors: Ibrahim, A., Gamble, P., Jaroensri, R., Abdelsamea, M. M., Mermel, C. H., Chen, P.-H. C., & Rakha, E. A.
Affiliations: Nottingham University Hospitals NHS Trust (GB)
Google (United States) (US)
Birmingham City University (GB)
Corresponding author: Rakha, Emad A.
ORCID: Ibrahim, Asmaa
Abdelsamea, Mohammed M.
Mermel, Craig H.
Chen, Po-Hsuan Cameron
Rakha, Emad A.
Venue: The Breast
Publisher: Elsevier BV
Published: 2020-02
Volume: 49
Pages: 267-273
ISSN: 0960-9776 (ISSN-L)
DOI: 10.1016/j.breast.2019.12.007
PMC: PMC7375550
OpenAlex: W2995106101
Cited by: 202
References: 81
Citations by year: 2026: 15 · 2025: 27 · 2024: 39 · 2023: 45 · 2022: 34 · 2021: 30 · 2020: 11
FWCI: 1.25
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: The Breast (publishedVersion, cc-by-nc-nd)
PubMed Central (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords: artificial intelligence, digital pathology, breast cancer, histopathological diagnosis, diagnostic precision
MeSH terms: Artificial Intelligence, Breast, Breast Neoplasms, Female, Humans, Image Interpretation, Computer-Assisted

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Artificial intelligence in digital breast pathology: Techniques and applications

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks