Structured crowdsourcing enables convolutional segmentation of histology images

Amgad, Mohamed; Elfandy, Habiba; Hussein, Hagar; Atteya, Lamees A; Elsebaie, Mai A T; Abo Elnasr, Lamia S; Sakr, Rokia A; Salem, Hazem S E; Ismail, Ahmed F; Saad, Anas M; Ahmed, Joumana; Elsebaie, Maha A T; Rahman, Mustafijur; Ruhban, Inas A; Elgazar, Nada M; Alagha, Yahya; Osman, Mohamed H; Alhusseiny, Ahmed M; Khalaf, Mariam M; Younes, Abo-Alela F; Abdulkarim, Ali; Younes, Duaa M; Gadallah, Ahmed M; Elkashash, Ahmad M; Fala, Salma Y; Zaki, Basma M; Beezley, Jonathan; Chittajallu, Deepak R; Manthey, David; Gutman, David A; Cooper, Lee A D

doi:10.1093/bioinformatics/btz083

Cited by 315AI in cancer detectionMedical Image Segmentation TechniquesCell Image Analysis Techniques

Structured crowdsourcing enables convolutional segmentation of histology images

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Amgad, Mohamed, Elfandy, Habiba, Hussein, Hagar, Atteya, Lamees A, Elsebaie, Mai A T, Abo Elnasr, Lamia S, Sakr, Rokia A, Salem, Hazem S E +23 more·2019·Bioinformatics

DOI 10.1093/bioinformatics/btz083PMC PMC6748796

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Abstract

MOTIVATION: While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images. RESULTS: We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-participant discordance was systematically evaluated, revealing low discordance for tumor and stroma, and higher discordance for more subjectively defined or rare tissue classes. Feedback provided by senior participants enabled the generation and curation of 20 000+ annotated tissue regions. Fully convolutional networks trained using these annotations were highly accurate (mean AUC=0.945), and the scale of annotation data provided notable improvements in image classification accuracy. AVAILABILITY AND IMPLEMENTATION: Dataset is freely available at: https://goo.gl/cNM4EL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Standardized open-source tools, collaborative multi-institutional annotation efforts, and AI-assisted annotation workflows reduce manual burden.
“Emerging solutions include standardized open-source tools (e.g., QuPath) [44], collaborative multi-institutional annotation efforts, and AI-assisted annotation workflows that reduce manual burden [45].”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [45]
The Discussion describes structured crowdsourcing in which non-experts perform much of the time-consuming annotation work, and Methods §2.7 describes slides from multiple institutes. The paper does not show AI assisting annotators to reduce manual work: its segmentation models were trained on crowdsourced annotations. QuPath is cited separately as [44], not attributed to this source. (LLM, conf=0.85)
Experimental — accuracy not yet measured
These studies showed there are correlations between characterizations of TIL patterns, as analyzed by computerized algorithms, and patient survival rates and groupings of patients based on subclasses of immunotypes.
“These studies showed there are correlations between characterizations of TIL patterns, as analyzed by computerized algorithms, and patient survival rates and groupings of patients based on subclasses of immunotypes.”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [181]

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Reinhard, E., Adhikhmin, M., Gooch, B., & Shirley, P. S. (2001) · IEEE Computer Graphics and Applications · doi:10.1109/38.946629 · Cited by 2,949 · see citing works
Revisiting the hallmarks of cancer.
Fouad, Y. A., & Aanei, C. M. (2017) · Europe PMC (PubMed Central) · Cited by 873 · see citing works
Features of triple-negative breast cancer
Plasilova, M. L., Hayse, B., Killelea, B. K., Horowitz, N. R., Chagpar, A. B., & Lannin, D. R. (2016) · Medicine · doi:10.1097/md.0000000000004614 · Cited by 201 · see citing works
Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data
Gutman, D. A., Cobb, J., Somanna, D., Park, Y., Wang, F., Kurç, T., Saltz, J. H., Brat, D. J., Cooper, L., & Kong, J. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001469 · Cited by 191 · see citing works
The Digital Slide Archive: A Software Platform for Management, Integration, and Analysis of Histology for Cancer Research
Gutman, D. A., Khalilia, M. A., Lee, S., Nalisnik, M., Mullen, Z., Beezley, J., Chittajallu, D. R., Manthey, D., & Cooper, L. (2017) · Cancer Research · doi:10.1158/0008-5472.can-17-0629 · Cited by 180 · see citing works
CROWDSOURCING IMAGE ANNOTATION FOR NUCLEUS DETECTION AND SEGMENTATION IN COMPUTATIONAL PATHOLOGY: EVALUATING EXPERTS, AUTOMATED METHODS, AND THE CROWD
Irshad, H., Montaser‐Kouhsari, L., WALTZ, G., Bucur, O., Nowak, J. A., Dong, F., Knoblauch, N., & Beck, A. H. (2014) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814644730_0029 · Cited by 137 · see citing works
Crowdsourcing in Computer Vision
Kovashka, A., Russakovsky, O., Fei-Fei, L., & Grauman, K. L. (2016) · now publishers, Inc. eBooks · doi:10.1561/9781680832136 · Cited by 76 · see citing works
Crowdsourcing in Computer Vision
Kovashka, A., Fei-Fei, L., Russakovsky, O., & Grauman, K. L. (2016) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000071 · Cited by 65 · see citing works
Quanti.us: a tool for rapid, flexible, crowd-based annotation of images
Hughes, A. J., Mornin, J. D., Biswas, S. K., Beck, L. E., Bauer, D. P., Raj, A., Bianco, S., & Gartner, Z. J. (2018) · Nature Methods · doi:10.1038/s41592-018-0069-0 · Cited by 64 · see citing works
Crowdsourcing scoring of immunohistochemistry images: Evaluating Performance of the Crowd and an Automated Computational Method
Irshad, H., Oh, E.-Y., Schmolze, D. B., Quintana, L. M., Collins, L. C., Tamimi, R. M., & Beck, A. H. (2017) · Scientific Reports · doi:10.1038/srep43286 · Cited by 40 · see citing works
A Review on the Applications of Crowdsourcing in Human Pathology
Alialy, R., Tavakkol, S., Tavakkol, E., Ghorbani-Aghbolaghi, A., Ghaffarieh, A., Kim, S. H., & Shahabi, C. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_65_17 · Cited by 21 · see citing works

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Research works printed but not linked: 2

Long J. et al (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp. 3431– 3440.
Su H. et al (2012) Crowdsourcing annotations for visual object detection. In: Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence. Vol. 1. Toronto, Ontario, Canada.

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Komura, D., Onoyama, T., Shinbo, K., Odaka, H., Hayakawa, M., Ochi, M., Herdiantoputri, R. R., Endo, H., Katoh, H., Ikeda, T., et al. (2023) · Patterns · doi:10.1016/j.patter.2023.100688 · Cited by 35 · see citing works
NDG-CAM: Nuclei Detection in Histopathology Images with Semantic Segmentation Networks and Grad-CAM
Altini, N., Brunetti, A., Puro, E., Taccogna, M. G., Saponaro, C., Zito, F. A., Summa, S. D., & Bevilacqua, V. (2022) · Bioengineering · doi:10.3390/bioengineering9090475 · Cited by 34 · see citing works
Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation
Schmidt, A., Morales-Álvarez, P., & Molina, R. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01929 · Cited by 32 · see citing works
Artificial intelligence-based digital pathology using H&E-stained whole slide images in immuno-oncology: from immune biomarker detection to immunotherapy response prediction
Zhang, J., Choi, H., Kim, Y., Park, J., Cho, S., Kim, E., Cho, A., Lee, J. Y., Choi, J., Low, C., et al. (2025) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2024-011346 · Cited by 34 · see citing works
Automatic Detection Metastasis in Breast Histopathological Images Based on Ensemble Learning and Color Adjustment
Luz, D. D. S., Lima, T. J. B., Silva, R., Magalhães, D. M. V., & Araújo, F. H. D. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103564 · Cited by 32 · see citing works
Learning from crowds in digital pathology using scalable variational Gaussian processes
López-Pérez, M., Amgad, M. T., Morales-Álvarez, P., Ruiz, P., Cooper, L., Molina, R., & Katsaggelos, A. K. (2021) · Scientific Reports · doi:10.1038/s41598-021-90821-3 · Cited by 31 · see citing works
Enhanced Pathology Image Quality with Restore–Generative Adversarial Network
Rong, R., Wang, S., Zhang, X., Wen, Z., Cheng, X. W., Jia, L., Yang, D. M., Xie, Y., Zhan, X., & Xiao, G. (2023) · American Journal Of Pathology · doi:10.1016/j.ajpath.2022.12.011 · Cited by 30 · see citing works
Weakly-Supervised Semantic Segmentation for Histopathology Images Based on Dataset Synthesis and Feature Consistency Constraint
Fang, Z., Chen, Y., Wang, Y., Wang, Z., Ji, X., & Zhang, Y. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i1.25136 · Cited by 30 · see citing works
A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide Images
Wang, H., Ahn, E., & Kim, J. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2024.110621 · Cited by 26 · see citing works
DETisSeg: A dual-encoder network for tissue semantic segmentation of histopathology image
He, P., Qu, A., Xiao, S., & Ding, M. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105544 · Cited by 27 · see citing works
A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study
Dudgeon, S. N., Wen, S., Hanna, M. G., Gupta, R., Amgad, M. T., Sheth, M., Marble, H. D., Huang, R., Herrmann, M. D., Szu, C. H., et al. (2021) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_83_20 · Cited by 29 · see citing works
Leveraging medical Twitter to build a visual–language foundation model for pathology AIPreprint
Huang, Z., Bianchi, F., Yüksekgönül, M., Montine, T. J., & Zou, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.29.534834 · Cited by 27 · see citing works
High expression of MKK3 is associated with worse clinical outcomes in African American breast cancer patients
Yang, X., Amgad, M. T., Cooper, L., Du, Y., Fu, H., & Ivanov, A. A. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-020-02502-w · Cited by 26 · see citing works
Robust Interactive Semantic Segmentation of Pathology Images with Minimal User Input
Jahanifar, M., Tajeddin, N. Z., Koohbanani, N. A., & Rajpoot, N. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00081 · Cited by 26 · see citing works
Unsupervised Representation Learning for Tissue Segmentation in Histopathological Images: From Global to Local Contrast
Gao, Z., Jia, C., Yang, L., Zhang, X., Hong, B., Wu, J., Gong, T., Wang, C., Meng, D., Zheng, Y., et al. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3191398 · Cited by 26 · see citing works
Consistency regularisation in varying contexts and feature perturbations for semi-supervised semantic segmentation of histology images
Bashir, R. M. S., Qaiser, T., Raza, S. E. A., & Rajpoot, N. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102997 · Cited by 26 · see citing works
A Data Augmentation Methodology to Reduce the Class Imbalance in Histopathology Images
Guerrero, R. E. D., Carvalho, L., Bocklitz, T. W., Popp, J., & Oliveira, J. L. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01018-9 · Cited by 30 · see citing works
A panoptic segmentation dataset and deep-learning approach for explainable scoring of tumor-infiltrating lymphocytes
Liu, S., Amgad, M. T., More, D., Rathore, M. A., Salgado, R., & Cooper, L. (2024) · npj Breast Cancer · doi:10.1038/s41523-024-00663-1 · Cited by 25 · see citing works
TPRO: Text-Prompting-Based Weakly Supervised Histopathology Tissue Segmentation
Zhang, S., Zhang, J., Xie, Y., & Xia, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43907-0_11 · Cited by 25 · see citing works
Deep Gaussian Processes for Classification With Multiple Noisy Annotators. Application to Breast Cancer Tissue Classification
López-Pérez, M., Morales-Álvarez, P., Cooper, L., Molina, R., & Katsaggelos, A. K. (2023) · IEEE Access · doi:10.1109/access.2023.3237990 · Cited by 24 · see citing works
A framework for falsifiable explanations of machine learning models with an application in computational pathology
Schuhmacher, D., Schörner, S., Küpper, C., Großerueschkamp, F., Sternemann, C., Lugnier, C., Kraeft, A., Jütte, H., Tannapfel, A., Reinacher‐Schick, A., et al. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102594 · Cited by 23 · see citing works
Reducing annotation burden through multimodal learning
Lopez, K., Fodeh, S. J., Allam, A., Brandt, C. A., & Krauthammer, M. (2020) · Frontiers in Big Data · doi:10.3389/fdata.2020.00019 · Cited by 21 · see citing works
Tumor Cellularity Assessment of Breast Histopathological Slides via Instance Segmentation and Pathomic Features Explainability
Altini, N., Puro, E., Taccogna, M. G., Marino, F., Summa, S. D., Saponaro, C., Mattioli, E., Zito, F. A., & Bevilacqua, V. (2023) · Bioengineering · doi:10.3390/bioengineering10040396 · Cited by 21 · see citing works
Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) With Diverse Inter-Correlations and Its Application to Medical Image Classification
Lai, Q., Zhou, J., Gan, Y., Vong, C., & Chen, C. L. P. (2023) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2023.3287978 · Cited by 21 · see citing works
STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics
Chen, J., Zhou, M., Wu, W., Zhang, J., Li, Y., & Li, D. (2024) · neural information processing systems · doi:10.52202/079017-1129 · Cited by 27 · see citing works
FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification
Deng, T., Huang, Y., Han, G., Shi, Z., Lin, J., Dou, Q., Liu, Z., Guo, X., Chen, C. L. P., & Han, C. (2024) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2024.3403927 · Cited by 24 · see citing works
Bi-VLGM: Bi-Level Class-Severity-Aware Vision-Language Graph Matching for Text Guided Medical Image Segmentation
Chen, W., Liu, J., Liu, T., & Yuan, Y. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02246-w · Cited by 20 · see citing works
Explainable synthetic image generation to improve risk assessment of rare pediatric heart transplant rejection
Giuste, F., Sequeira, R. C., Keerthipati, V., Lais, P. F., Mirzazadeh, A., Mohseni, A., Zhu, Y., Shi, W., Marteau, B. L., Zhong, Y., et al. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104303 · Cited by 20 · see citing works
Realistic Evaluation of Deep Active Learning for Image Classification and Semantic Segmentation
Mittal, S., Niemeijer, J., Çiçek, Ö., Tatarchenko, M., Ehrhardt, J., Schäfer, J. P., Handels, H., & Brox, T. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02372-z · Cited by 24 · see citing works
Identify Consistent Imaging Genomic Biomarkers for Characterizing the Survival-Associated Interactions Between Tumor-Infiltrating Lymphocytes and Tumors
Zuo, Y., Wu, Y., Lu, Z., Zhu, Q., Huang, K., Zhang, D., & Shao, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16434-7_22 · Cited by 19 · see citing works
A Weakly Supervised Deep Learning Framework for Whole Slide Classification to Facilitate Digital Pathology in Animal Study
Bussola, N., Xu, J., Wu, L., Gorini, L., Zhang, Y., Furlanello, C., & Tong, W. (2023) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.3c00058 · Cited by 19 · see citing works
Toward a generalizable machine learning workflow for neurodegenerative disease staging with focus on neurofibrillary tangles
Vizcarra, J. C., Pearce, T. M., Dugger, B. N., Keiser, M. J., Gearing, M., Crary, J. F., Kiely, E. J., Morris, M., White, B., Glass, J. D., et al. (2023) · Acta Neuropathologica Communications · doi:10.1186/s40478-023-01691-x · Cited by 19 · see citing works
Automated scoring methods for quantitative interpretation of Tumour infiltrating lymphocytes (TILs) in breast cancer: a systematic review
Baharun, N. B., Adam, A., Zailani, M. A. H., Rajpoot, N., Xu, Q., & Zin, R. R. M. (2024) · BMC Cancer · doi:10.1186/s12885-024-12962-8 · Cited by 19 · see citing works
Leveraging image complexity in macro-level neural network design for medical image segmentation
Khan, T. M., Naqvi, S. S., & Meijering, E. H. W. (2022) · Scientific Reports · doi:10.1038/s41598-022-26482-7 · Cited by 22 · see citing works
Leveraging advances in immunopathology and artificial intelligence to analyze in vitro tumor models in composition and space
Leong, T. K. M., Lo, W. S., Lee, W. E. Z., Tan, B., Lee, X. Z., Lee, L. W. J. N., Lee, J.-Y. J., Suresh, N., Loo, L., Szu, E., et al. (2021) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2021.113959 · Cited by 19 · see citing works
Shortcomings and areas for improvement in digital pathology image segmentation challenges
Foucart, A., Debeir, O., & Decaestecker, C. (2022) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2022.102155 · Cited by 18 · see citing works
Characterizing the Survival-Associated Interactions Between Tumor-Infiltrating Lymphocytes and Tumors From Pathological Images and Multi-Omics Data
Shao, W., Zuo, Y., Shi, Y., Wu, Y., Tang, J., Zhao, J., Sun, L., Lu, Z., Sheng, J., Zhu, Q., et al. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3274652 · Cited by 18 · see citing works
Automated Detection and Scoring of Tumor-Infiltrating Lymphocytes in Breast Cancer Histopathology Slides
Yosofvand, M., Khan, S. Y., Dhakal, R., Nejat, A., Moustaïd‐Moussa, N., Rahman, R. L., & Moussa, H. M. (2023) · Cancers · doi:10.3390/cancers15143635 · Cited by 18 · see citing works
Expression site agnostic histopathology image segmentation framework by self supervised domain adaption
He, Q., He, L., Duan, H., Sun, Q., Zheng, R., Guan, J., He, Y., Huang, W., & Guan, T. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106412 · Cited by 17 · see citing works
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 · see citing works
Future of Breast Cancer Diagnosis: A Review of DL and ML Applications and Emerging Trends for Multimodal Data
Abdikenov, B., Zhaksylyk, T., Shortanbaiuly, O., Orazayev, Y., Makhanov, N., Karibekov, T., Suvorov, V., Imasheva, A., Zhumagozhayev, K., & Seitova, A. (2025) · IEEE Access · doi:10.1109/access.2025.3585377 · Cited by 16 · see citing works
Reliable identification and quantification of neural cells in microscopic images of neurospheres
Förster, N., Butke, J., Keßel, H. E., Bendt, F., Pahl, M., Li, L., Fan, X., Leung, P., Klose, J., Masjosthusmann, S., et al. (2021) · Cytometry Part A · doi:10.1002/cyto.a.24514 · Cited by 16 · see citing works
Semi-supervised breast cancer pathology image segmentation based on fine-grained classification guidance
Sun, K., Zheng, Y., Yang, X., Chen, X., & Jia, W. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02970-4 · Cited by 16 · see citing works
TestFit: A plug-and-play one-pass test time method for medical image segmentation
Zhang, Y., Zhou, T., Tao, Y., Wang, S., Wu, Y., Liu, B., Gu, P., Chen, Q., & Chen, D. Z. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103069 · Cited by 17 · see citing works
OralEpitheliumDB: A Dataset for Oral Epithelial Dysplasia Image Segmentation and Classification
Silva, A. B., Martins, A. S., Tosta, T. A. A., Loyola, A. M., Cardoso, S. V., Neves, L. A., de Faria, P. R., & do Nascimento, M. Z. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01041-w · Cited by 16 · see citing works

Learning from crowds for automated histopathological image segmentation
López-Pérez, M., Morales-Álvarez, P., Cooper, L., Felicelli, C., Goldstein, J., Vadasz, B., Molina, R., & Katsaggelos, A. K. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102327 · Cited by 9 · see citing works
Seeded iterative clustering for histology region identificationPreprint
Chelebian, E., Ciompi, F., & Wählby, C. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2211.07425 · Cited by 1 · see citing works
Generative modeling of histology tissue reduces human annotation effort for segmentation model development
Lutnick, B. R., Lucarelli, N., & Sarder, P. (2023) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2655282 · Cited by 1 · see citing works
Crowdsourcing of Histological Image Labeling and Object Delineation by Medical Students
Grote, A., Schaadt, N. S., Forestier, G., Wemmert, C., & Feuerhake, F. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2883237 · Cited by 34 · see citing works
Crowdsourcing Segmentation of Histopathological Images Using Annotations Provided by Medical Students
López-Pérez, M., Morales-Álvarez, P., Cooper, L., Molina, R., & Katsaggelos, A. K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34344-5_29 · Cited by 2 · see citing works

Authors: Amgad, M., Elfandy, H., Hussein, H., Atteya, L. A., Elsebaie, M. A. T., Abo Elnasr, L. S., Sakr, R. A., Salem, H. S. E., Ismail, A. F., Saad, A. M., Ahmed, J., Elsebaie, M. A. T., Rahman, M., Ruhban, I. A., Elgazar, N. M., Alagha, Y., Osman, M. H., Alhusseiny, A. M., Khalaf, M. M., Younes, A.-A. F., Abdulkarim, A., Younes, D. M., Gadallah, A. M., Elkashash, A. M., Fala, S. Y., Zaki, B. M., Beezley, J., Chittajallu, D. R., Manthey, D., Gutman, D. A., & Cooper, L. A. D.
Affiliations: Emory University (US)
Department of Pathology, National Cancer Institute , Cairo, Egypt
Cairo University (EG)
Ministry of Health and Population (EG)
Ain Shams University (EG)
Department of Medicine, Menoufia University , Menoufia, Egypt
Menoufia University (EG)
Alexandria University (EG)
University of Chittagong (BD)
Damascus University (SY)
Mansoura University (EG)
Zagazig University (EG)
Batterjee Medical College (SA)
Suez Canal University (EG)
Kitware (United States) (US)
Corresponding author: Cooper, Lee A D
ORCID: Amgad, Mohamed
Elfandy, Habiba
Hussein, Hagar
Elsebaie, Mai A T
Sakr, Rokia A
Salem, Hazem S E
Saad, Anas M
Elsebaie, Maha A T
Rahman, Mustafijur
Osman, Mohamed H
Alhusseiny, Ahmed M
Abdulkarim, Ali
Gadallah, Ahmed M
Chittajallu, Deepak R
Manthey, David
Gutman, David A
Cooper, Lee A D
Venue: Bioinformatics
Publisher: Oxford University Press (OUP)
Published: 2019-02-06
Volume: 35
Issue: 18
Pages: 3461-3467
ISSN: 1367-4803 (ISSN-L)
1367-4811
DOI: 10.1093/bioinformatics/btz083
PMC: PMC6748796
OpenAlex: W2922239620
Cited by: 315
References: 11
Citations by year: 2026: 41 · 2025: 73 · 2024: 61 · 2023: 60 · 2022: 42 · 2021: 20 · 2020: 17 · 2019: 1
FWCI: 9.12
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Bioinformatics (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Greater South Information System
Funders: U.S. National Institutes of Health
National Cancer Institute grants (U01CA220401, U24CA194362)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
Keywords: breast cancer, whole slide images, annotated dataset, fully convolutional network
MeSH terms: Algorithms, Breast Neoplasms, Histological Techniques, Humans, Crowdsourcing

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