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

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Structured crowdsourcing enables convolutional segmentation of histology images

Open access

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·2019·Bioinformatics

DOI 10.1093/bioinformatics/btz083PMC PMC6748796

Open this paper in CoChat — free account Open PDF

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.

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 · 10.1093/bioinformatics/btz083

Cites

11 of 11 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.

Color transfer between images
Reinhard, E., Adhikhmin, M., Gooch, B., & Shirley, P. S. (2001) · IEEE Computer Graphics and Applications · doi:10.1109/38.946629 · Cited by 2,949
Revisiting the hallmarks of cancer.
Fouad, Y. A., & Aanei, C. M. (2017) · Europe PMC (PubMed Central) · Cited by 875
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 211
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
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
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 141
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
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 75
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 65
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

Flagged first, then by citations

Show all 11 references

Research works printed but not linked: 1

Su, 2012, Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence

Sorted by citations

Items 101–200 of 315

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
Tumor Micro-Environment Interactions Guided Graph Learning for Survival Analysis of Human Cancers from Whole-Slide Pathological Images
Shao, W., Shi, Y., Zhang, D., Zhou, J., & Wan, P. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01111 · Cited by 16
Deep embeddings and logistic regression for rapid active learning in histopathological images
Jiao, Y., Yuan, J., Qiang, Y., & Fei, S. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106464 · Cited by 14
Artificial intelligence enhances whole‐slide interpretation of PD‐L1 CPS in triple‐negative breast cancer: A multi‐institutional ring study
Li, J., Dong, P., Wang, X., Zhang, J., Zhao, M. Y., Shen, H., Cai, L., He, J., Han, M., Miao, J., et al. (2024) · Histopathology · doi:10.1111/his.15205 · Cited by 13
Breast cancer detection based on histological images using fusion of diffusion model outputs
Akbari, Y., Abdullakutty, F. C., Al-Maadeed, S. A., Bouridane, A., & Hamoudi, R. (2025) · Scientific Reports · doi:10.1038/s41598-025-05744-0 · Cited by 14
Histokt: Cross Knowledge Transfer in Computational Pathology
Zhang, R., Zhu, J., Yang, S. C., Hosseini, M. S., Genovese, A., Chen, L., Rowsell, C. H., Damaskinos, S., Varma, S., & Plataniotis, K. N. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747400 · Cited by 13
Fourier ViT: A Multi-scale Vision Transformer with Fourier Transform for Histopathological Image Classification
Duan, H., Liu, Y., Yan, H., He, Q., He, Y., & Guan, T. (2022) · International Conference on Automation, Control and Robotics Engineering (CACRE) · doi:10.1109/cacre54574.2022.9834158 · Cited by 12
Human-Machine Interactive Tissue Prototype Learning for Label-Efficient Histopathology Image Segmentation
Pan, W., Yan, J., Chen, H., Yang, J., Xu, Z., Li, X., & Yao, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34048-2_52 · Cited by 14
PathMamba: Weakly Supervised State Space Model for Multi-class Segmentation of Pathology Images
Fan, J., Lv, T., Di, Y., Li, L., & Pan, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72111-3_47 · Cited by 12
HisynSeg: Weakly-Supervised Histopathological Image Segmentation via Image-Mixing Synthesis and Consistency Regularization
Fang, Z., Wang, Y., Xie, P., Wang, Z., & Zhang, Y. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3520129 · Cited by 13
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
A panoptic segmentation approach for tumor-infiltrating lymphocyte assessment: development of the MuTILs model and PanopTILs datasetPreprint
Liu, S., Amgad, M. T., Rathore, M. A., Salgado, R., & Cooper, L. (2022) · medRxiv · doi:10.1101/2022.01.08.22268814 · Cited by 12
HistomicsTK: A Python toolkit for pathology image analysis algorithms
Pourakpour, F., Szölgyén, Á., Nateghi, R., Gutman, D. A., Manthey, D., & Cooper, L. (2025) · SoftwareX · doi:10.1016/j.softx.2025.102318 · Cited by 11
Effective and efficient active learning for deep learning-based tissue image analysis
Meirelles, A., Kurç, T., Kong, J., Ferreira, R., Saltz, J., & Teodoro, G. (2023) · Bioinform. · doi:10.1093/bioinformatics/btad138 · Cited by 11
Enhancing AI Research for Breast Cancer: A Comprehensive Review of Tumor-Infiltrating Lymphocyte Datasets
Fiorin, A., López, C., Lejeune, M., Siraj, A. H., & Mea, V. D. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01043-8 · Cited by 11
Mining Gold from the Sand: Weakly Supervised Histological Tissue Segmentation with Activation Relocalization and Mutual Learning
Feng, S., Chen, J., Liu, Z., Liu, W., Wang, Z., Lan, R., & Pan, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72111-3_39 · Cited by 11
SC-Net: Symmetrical conical network for colorectal pathology image segmentation
Zhang, G., He, Z., Zhang, Y., Li, Z., & Wu, L. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108119 · Cited by 11
Using Sparse Patch Annotation for Tumor Segmentation in Histopathological Images
Liu, Y., He, Q., Duan, H., Shi, H., Han, A., & He, Y. (2022) · Sensors · doi:10.3390/s22166053 · Cited by 10
On Measures of Uncertainty in Classification
Chlaily, S., Ratha, D., Lozou, P., & Marinoni, A. (2023) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2023.3322843 · Cited by 10
PCSformer: Pair-wise Cross-scale Sub-prototypes mining with CNN-transformers for weakly supervised semantic segmentation
Liu, C., Shen, Y., Xiao, Q., & Li, G. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127834 · Cited by 10
Enhancing Weakly Supervised Semantic Segmentation With Multi-Label Contrastive Learning and LLM Features Guidance
Cai, W., Li, Y., Chen, Y., Lin, J., Huang, Z., Gao, P., Gadekallu, T. R., Wang, W. W., & Gao, Y. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3450013 · Cited by 10
SegAnyPath: A Foundation Model for Multi- Resolution Stain-Variant and Multi-Task Pathology Image Segmentation
Wang, C., Wan, Y., Li, S., Qu, K., Zhou, X., He, J., Ke, J., Yu, Y., Wang, T., & Shen, Y. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3501352 · Cited by 13
DIPathMamba: A domain-incremental weakly supervised state space model for pathology image segmentation
Fan, J., Sun, Q., Di, Y., Bao, J., Lv, T., Liu, Y., Hu, X., Li, L., Cui, X., & Pan, X. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103563 · Cited by 10
Transformers in pathological image analysis: A survey
Liu, L., Liu, Z., Xie, J., Zhang, H., Chang, J., & Qiao, H. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111114 · Cited by 11
EndoNuke: Nuclei Detection Dataset for Estrogen and Progesterone Stained IHC Endometrium Scans
Naumov, A., Ushakov, E. N., Ivanov, A., Midiber, K. Y., Николаевна, Х. Т., Konyukova, A. K., Vishnyakova, P., Nora, S. A., Михалева, Л. М., Fatkhudinov, T., et al. (2022) · Data · doi:10.3390/data7060075 · Cited by 10
HistomicsML2.0: Fast interactive machine learning for whole slide imaging dataPreprint
Lee, S., Amgad, M. T., Chittajallu, D. R., McCormick, M., Pollack, B. P., Elfandy, H., Hussein, H., Gutman, D. A., & Cooper, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.11547 · Cited by 10
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 11
Annotation-efficient deep learning for breast cancer whole-slide image classification using tumour infiltrating lymphocytes and slide-level labels
Perera, R. D., Savas, P. S., Senanayake, D., Salgado, R., Joensuu, H., O’Toole, S. A., Li, J., Loi, S., & Halgamuge, S. K. (2024) · Communications Engineering · doi:10.1038/s44172-024-00246-9 · Cited by 9
Dual multi scale networks for medical image segmentation using contrastive learning
Akshat, D., Rajalakshmi, R., & Balasundaram, A. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105371 · Cited by 9
Annotation protocol and crowdsourcing multiple instance learning classification of skin histological images: The CR-AI4SkIN dataset
del Amor, R., Pérez-Cano, J., López-Pérez, M., Terrádez-Mas, L., Aneiros‐Fernández, J., Morales, S., Mateos, J., Molina, R., & Naranjo, V. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102686 · Cited by 9
Precise Location Matching Improves Dense Contrastive Learning in Digital Pathology
Zhang, J., Kapse, S., Ma, K., Prasanna, P., Vakalopoulou, M., Saltz, J. H., & Samaras, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34048-2_60 · Cited by 9
Enhancing Histopathology Breast Cancer Detection and Classification with the Deep Ensemble Graph Network
Shwetha, G. K., & Reddy, K. R. U. K. (2024) · SN Computer Science · doi:10.1007/s42979-024-02855-9 · Cited by 8
Tissue concepts: Supervised foundation models in computational pathology
Nicke, T., Schäfer, J. R., Höfener, H., Feuerhake, F., Merhof, D., Kiessling, F. M., & Lotz, J. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109621 · Cited by 17
Classifying tumour infiltrating lymphocytes in oral squamous cell carcinoma histopathology using joint learning framework
Barua, B., Chyrmang, G., Bora, K., Ahmed, G. N., Kakoti, L., & Saikia, M. J. (2025) · Scientific Reports · doi:10.1038/s41598-025-86527-5 · Cited by 8
Revisiting medical image retrieval via knowledge consolidation
Yang, N., Zhou, H., Xing, X., Papanastasiou, G., Zhu, L., Gao, Z., Frangi, A. F., & Yang, G. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103553 · Cited by 8
Adaptive decoupling planning method for the product crowdsourcing design tasks based on knowledge reuse
Gao, X., Feng, Y., Hong, Z., Mi, S., & Tan, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117525 · Cited by 8
Robust High-Throughput Phenotyping with Deep Segmentation Enabled by a Web-Based Annotator
Yuan, J., Kaur, D., Zhou, Z., Nagle, M. F., Kiddle, N. G., Doshi, N. A., Behnoudfar, A., Peremyslova, E., Ma, C., Strauss, S. H., et al. (2022) · Plant Phenomics · doi:10.34133/2022/9893639 · Cited by 8
Interpretable machine learning text classification for clinical computed tomography reports – a case study of temporal bone fracture
Tong, L., Jake, L., Adams, J. A., Osinski, K. I., Liu, X., & Friedland, D. R. (2023) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2023.100104 · Cited by 10
Comprehensive Experiments on Breast Cancer Hematoxylin and Eosin-stained Images Using UNet
Jackson, E., Le, F.-L., Lisbon, J. I., Coleman, M., Burman, J., Wonderley, A., Eshaghian, S., & Lee, S. (2024) · ACM Southeast Regional Conference (ACMSE) · doi:10.1145/3603287.3651207 · Cited by 8
A method for balancing a multi-labeled biomedical dataset
Mukhin, A., Kilbas, I., Александрович, П. Р., Ilyasova, N. Y., & Kupriyanov, A. V. (2022) · Integrated Computer-Aided Engineering · doi:10.3233/ica-220676 · Cited by 8
An Explainable Multimodal Artificial Intelligence Model Integrating Histopathological Microenvironment and EHR Phenotypes for Germline Genetic Testing in Breast Cancer
Yang, Z., Guo, C., Li, J., Li, Y., Zhong, L., Pu, P., Shang, T., Lin, C., Zhou, Y., Qiao, G., et al. (2025) · Advanced Science · doi:10.1002/advs.202502833 · Cited by 9
Cheminformatics and Bioinformatics at the Interface with Systems Biology
Kaushik, A. C., Mehmood, A., Wei, D., Nawab, S., Sahi, S., & Kumar, A. (2023) · Royal Society of Chemistry eBooks · doi:10.1039/9781839166037 · Cited by 7
SN-FPN: Self-Attention Nested Feature Pyramid Network for Digital Pathology Image Segmentation
Lee, S., Islam, K. A., Koganti, S. C., Mamillapalli, S. R. S., Yaganti, V., Vitalos, H., & Williamson, D. F. K. (2024) · IEEE Access · doi:10.1109/access.2024.3423701 · Cited by 7
A Review of Advanced Deep Learning Methods of Multi-Target Segmentation for Breast Cancer WSIs
Xu, Q., Adam, A., Abdullah, A., & Baharun, N. B. (2025) · IEEE Access · doi:10.1109/access.2025.3565648 · Cited by 6
Weakly supervised histopathology tissue semantic segmentation with multi-scale voting and online noise suppression
Pan, X., Zhang, H., Deng, H., Wang, H., Li, L., Liu, Z., Wang, L., An, Y., Lu, C. Y., Liu, Z., et al. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111100 · Cited by 8
An Automated Pipeline for Tumour-Infiltrating Lymphocyte Scoring in Breast Cancer
Shephard, A., Jahanifar, M., Wang, R., Dawood, M., Graham, S., Šidlauskas, K., Khurram, S. A., Rajpoot, N., & Raza, S. E. A. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635302 · Cited by 6
Stain SAN: simultaneous augmentation and normalization for histopathology images
Kim, T., Li, Y., Calhoun, B. C., Thennavan, A., Carey, L., Symmans, W. F., Troester, M. A., Perou, C. M., & Marron, J. S. (2024) · Journal of medical imaging · doi:10.1117/1.jmi.11.4.044006 · Cited by 6
The CrowdGleason dataset: Learning the Gleason grade from crowds and experts
López-Pérez, M., Morquecho, A., Schmidt, A., Pérez-Bueno, F., Castro, A. M., Mateos, J., & Molina, R. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108472 · Cited by 10
ProGIS: Prototype-Guided Interactive Segmentation for Pathological Images
Ge, J., Zhang, D., Zhan, Y., Liu, J., Gong, T., Wu, J., Crispin-Ortuzar, M., Li, C., & Gao, Z. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3611123 · Cited by 6
The Beauty or the Beast: Which Aspect of Synthetic Medical Images Deserves Our Focus?
Xing, X., Nan, Y., Felder, F., Walsh, S. L. F., & Yang, G. (2023) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms58004.2023.00273 · Cited by 6
A Generic Neural Network Approach to Infer Segmenting Classifiers for Disease-Associated Regions in Medical ImagesPreprint
Schuhmacher, D., Gerwert, K. B., & Mosig, A. (2020) · medRxiv · doi:10.1101/2020.02.27.20028845 · Cited by 6
Multiclass Semantic Segmentation of Immunostained Breast Cancer Tissue with a Deep-Learning ApproachPreprint
Ortega-Ruíz, M. A., Román-Rangel, E., & Reyes‐Aldasoro, C. C. (2022) · medRxiv · doi:10.1101/2022.08.17.22278889 · Cited by 6
ToPoFM: Topology-Guided Pathology Foundation Model for High-Resolution Pathology Image Synthesis With Cellular-Level Control
Li, J., Zhu, C., Zheng, S., Chen, P., Sun, Y., Li, H., & Yang, L. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3548872 · Cited by 6
Uncertainty-guided cross teaching semi-supervised framework for histopathology image segmentation with curriculum self-training
Xu, R., Zhou, N., Feng, S., Liu, Z., Deng, H., An, Y., Li, J., Han, C., Liu, Z., Lan, R., et al. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113328 · Cited by 5
A novel virtual patient approach for cross-patient multimodal fusion in enhanced breast cancer detection
Akbari, Y., Abdullakutty, F. C., Al-Maadeed, S. A., Al-Saady, R., Bouridane, A., & Hamoudi, R. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102687 · Cited by 5
Pathology Public Datasets for Artificial Intelligence: A Systematic Review
Chinnachinnanagari, A. R., Debsarkar, S. S., & Prasath, V. B. S. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-01899-y · Cited by 7
An interpretable framework for inter-observer agreement measurements in TILs scoring on histopathological breast images: A proof-of-principle study
Çapar, A., Ekinci, D. A., Ertano, M., Niazi, M. K. K., Balaban, E. B., Aloglu, I., Doğan, M., Su, Z., Aker, F., & Gürcan, M. N. (2024) · PLoS ONE · doi:10.1371/journal.pone.0314450 · Cited by 5
Probeable DARTS with Application to Computational Pathology
Tang, S., Hosseini, M. S., Chen, L., Varma, S., Rowsell, C. H., Damaskinos, S., Plataniotis, K. N., & Wang, Z. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00070 · Cited by 5
Rethinking Quality Assurance for Crowdsourced Multi-ROI Image Segmentation
Lu, X., Ratcliffe, D., Kao, T.-T., Tikhonov, A., Litchfield, L., Rodger, C. J., & Wang, K. (2023) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v11i1.27552 · Cited by 5
Synthetic Biology: Fostering the Cyber-biological Revolution
(2023) · Royal Society of Chemistry eBooks · doi:10.1039/9781839166037-00196 · Cited by 4
Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images
Lv, J., Zhu, Y., Tenorio, C. G. C., Chohan, B. S., Eastwood, M., & Raza, S. E. A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98688-8_5 · Cited by 4
Supporting intraoperative margin assessment using deep learning for automatic tumour segmentation in breast lumpectomy micro-PET-CT
Maris, L., Göker, M., Man, K. D., den Broeck, B. V., Hoecke, S. V., de Vijver, K. K. V., Vanhove, C., & Keereman, V. (2025) · npj Breast Cancer · doi:10.1038/s41523-025-00797-w · Cited by 4
HistoCLIP: Multilabel Classification Framework for Histopathological Images Based on Cross-Modal Matching Learning
Bai, B., & Miyata, K. (2025) · IEEE Access · doi:10.1109/access.2025.3609221 · Cited by 4
PLIMSeg: Pathology language–image matching for weakly supervised semantic segmentation of histopathology images
Ding, M., Li, X., Chen, W., Deng, S., Shen, L., & Lai, Z. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108669 · Cited by 4
Synergizing Deep Learning-Enabled Preprocessing and Human–AI Integration for Efficient Automatic Ground Truth Generation
Collazo, C., Vargas, I., Cara, B. D., Weinheimer, C. J., Grabau, R. P., Goldgof, D. B., Hall, L., Wickline, S. A., & Pan, H. (2024) · Bioengineering · doi:10.3390/bioengineering11050434 · Cited by 5
分割一切模型（SAM）在医学图像分割中的应用
Tong, 吴. W., Haoji, 胡. H., Yang, 冯. F., Qiong, 罗. L., Dong, 徐. X., Weizeng, 郑. Z., Neng, 金. J., Chen, 杨. Y., & Jincao, 姚. Y. (2024) · Chinese Journal of Lasers · doi:10.3788/cjl240614 · Cited by 5
Exploring Unbiased Activation Maps for Weakly Supervised Tissue Segmentation of Histopathological Images
Kang, Y., Li, H., Shi, X., Zhang, X., Xing, Y., Wen, Y., Wang, Y., Cui, L., Feng, J. H., & Yang, L. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3541115 · Cited by 4
Expert knowledge-guided multi-granularity multi-scale fusion for weakly-supervised histological segmentation
Hu, X., Cao, Y., Hu, W., Wang, S., Mukhopadhyay, S. C., Liu, Y., Li, H., Li, Y., Cai, Q., & Liu, Z. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103432 · Cited by 4
Weakly supervised semantic segmentation of histological tissue via attention accumulation and pixel-level contrast learning
Han, Y., Cheng, L., Huang, G., Zhong, G., Li, J., Yuan, X., Liu, H., Li, J., Zhou, J., & Cai, M. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/acaeee · Cited by 4
A distributed system improves inter-observer and AI concordance in annotating interstitial fibrosis and tubular atrophy
Shashiprakash, A. K., Lutnick, B. R., Ginley, B. G., Govind, D., Lucarelli, N., Jen, K., Rosenberg, A. Z., Urisman, A., Walavalkar, V., Zuckerman, J. E., et al. (2021) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2581789 · Cited by 4
Assessing Representation Learning and Clustering Algorithms for Computer-Assisted Image Annotation—Simulating and Benchmarking MorphoCluster
Schröder, S., & Kiko, R. (2022) · Sensors · doi:10.3390/s22072775 · Cited by 6
Explainable patch-level histopathology tissue type detection with bag-of-local-features models and data augmentation
Galiger, G., & Bodó, Z. (2023) · Acta Universitatis Sapientiae Informatica · doi:10.2478/ausi-2023-0006 · Cited by 6
Quantitative Spatial Analysis on Whole Slide Images Using U-Net
Lee, S., Zhao, Y., Masoud, M., & Belkasim, S. O. (2020) · Computational Biology and Bioinformatics · doi:10.11648/j.cbb.20200802.18 · Cited by 4
Inspect quantitative signals in placental histopathology: Computer-assisted multiple functional tissues identification through multi-model fusion and distillation framework
Liu, Y., Zhang, L., Gu, M., Xiao, Y., Yu, T., Tao, X., Zhang, Q., Wang, Y., Shen, D. G., & Li, Q. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102482 · Cited by 3
Towards gastric cancer pathological segmentation: A large-scale whole-slide images dataset and dual-stream mask guided attention U-net
Zhang, Q., Xiong, J., Wang, Y., Luo, T.-J., Gao, W., & Lin, Y. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108398 · Cited by 5
The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation
Akinniyi, O., Dixon, J., Aina, J., Weaks, F., Saleh, G. A., Rahman, M. M., Meeker, T. J., Trivedi, H., Gichoya, J. W., & Khalifa, F. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109036 · Cited by 3
Edge-Semantic Synergy Fusion and Adaptive Noise-Aware for Weakly Supervised Pathological Tissue Segmentation
Zhang, H., Feng, S., Huan, Z., Wang, H., Liu, Z., Lan, R., & Pan, X. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04984-1_16 · Cited by 3
Dynamic model scaling based on segmented tumor size for breast cancer detection
Akbari, Y., Abdullakutty, F. C., Al-Maadeed, S. A., Bouridane, A., & Hamoudi, R. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109118 · Cited by 6
Enhanced colorectal gland segmentation through multi-scale attention and contextual feature fusion
Ponnarasee, B. K., Lalithamani, N., & Ajibesin, A. A. (2026) · Scientific Reports · doi:10.1038/s41598-025-34548-5 · Cited by 3
Tumor microenvironment transcriptional activity enables robust stratification of chemotherapy response in triple-negative breast cancer
Dai, Y., Pan, X., Guo, S., Ji, S., Cao, S., Montierth, M. D., Jiang, Y., Chang, J. T., Shi, L., Shalapour, S., et al. (2026) · Cell Reports Medicine · doi:10.1016/j.xcrm.2026.102610 · Cited by 3
AutoPathML: Automated Machine Learning for Histology Images via Large Language Model and Multi‐Agent
Shen, Y., Wang, C., & Ke, J. (2025) · Artificial Intelligence for Engineering · doi:10.1049/aie2.12005 · Cited by 4
Display systems for digital pathology: what are proper luminance, contrast and resolution settings?
Vasudev, V., Xthona, A., & Kimpe, T. R. L. (2022) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2611416 · Cited by 3
Malignant breast lesions detection in histopathological images based on the combination of bioinspired texture descriptors and deep features
Luz, D. D. S., Bisneto, T. R. V., Lima, T. J. B., Mathew, M. J., & Araújo, F. H. D. (2023) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2023.2198037 · Cited by 3
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
Superresolved spatial transcriptomics transferred from a histological context
Wang, S., Zhou, X., Kong, Y., & Lu, H. J. (2023) · Applied Intelligence · doi:10.1007/s10489-023-05190-3 · Cited by 2
Rethinking deep active learning for medical image segmentation: A diffusion and angle-based framework
Qu, L., Jin, Q., Fu, K., Wang, M., & Song, Z. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106493 · Cited by 3
CPIA dataset: a large-scale comprehensive pathological image analysis dataset for self-supervised learning pre-training
Ying, N., Lei, Y., Zhang, T., Lyu, S., Chen, S., Liu, Z., Feng, Y., Zhao, Y., & Zhang, G. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108148 · Cited by 3
Tumor Infiltrating Lymphocytes in Breast Cancer: Implementation of a New Histopathological Biomarker
Floris, G., Broeckx, G., Antoranz, A., Schepper, M. D., Salgado, R., Desmedt, C., Peeters, D. J. E., & den Eynden, G. G. G. M. V. (2022) · Springer eBooks · doi:10.1007/978-3-030-98950-7_13 · Cited by 4
Multiple-Instance Learning with Efficient Transformer for Breast Tumor Image Classification in Bright Challenge
Wentai, F., Jinbo, K., Ji, Z., & Shuoyu, X. (2022) · doi:10.1109/isbic56247.2022.9854532 · Cited by 3
Leveraging Image Complexity in Macro-Level Neural Network Design for Medical Image SegmentationPreprint
Khan, T. M., Naqvi, S. S., & Meijering, E. H. W. (2022) · Research Square · doi:10.21203/rs.3.rs-1833303/v1 · Cited by 3
A fusocelular skin dataset with whole slide images for deep learning models
del Amor, R., López-Pérez, M., Meseguer, P., Morales, S., Terrádez-Mas, L., Aneiros‐Fernández, J., Javier Mateos, A., Molina, R., & Naranjo, V. (2025) · Scientific Data · doi:10.1038/s41597-025-05108-3 · Cited by 3
Active learning in computational pathology with noise detection empowered by loss-based prior and feature analysis
Huang, Y., Li, J., An, H., Luo, T., Ji, Z., Song, Y. F., Liu, H., Kaicheng, U., Zhang, S. M., Zhao, J., et al. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107953 · Cited by 2
HLIP: A pan-cancer model for histological image analysis in clinical research using TCGA
Rong, J., Zhong, H., Meng, Y., Jin, Q., Zhang, Y., & Zuo, C. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2025.108589 · Cited by 2
Pathology Image Compression with Pre-trained Autoencoders
Yellapragada, S., Graikos, A., Triaridis, K., Li, Z., Nandi, T., Madduri, R., Prasanna, P., Saltz, J. H., & Samaras, D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04937-7_42 · Cited by 2
A systematic review of semantic segmentation methods for histopathology images: a focused survey on breast, colon, and prostate cancers
Tan, D. I. T. M. H., Tan, X. J., Lim, L. L., Rahman, K. S. A., & Siet, J. J. W. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06906-3 · Cited by 2
Wave-Aware Weakly Supervised Histopathological Tissue Segmentation With Cross-Scale Logits Distillation
Feng, S., Zhang, H., Zhao, X., Shi, L., Liu, Z., Lan, R., Shi, L., & Pan, X. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3637119 · Cited by 2
Label Noise in Pathological Segmentation Is Overlooked, Leading to Potential Overestimation of Artificial Intelligence
Harada, K., Nomura, Y., Komura, D., Ishikawa, S., & Sakashita, S. (2025) · Cancer Science · doi:10.1111/cas.70288 · Cited by 2
Elevating Medical Image Security: A Cryptographic Framework Integrating Hyperchaotic Map and GRU
Li, W., Yu, G., Li, Q., Zhou, J., Chen, J., Dong, Y., Wang, M., Li, Z., Gong, C., Tang, L., et al. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356029 · Cited by 2
MSB-UNet: A Multi-Scale Bifurcation U-Net Architecture for Precise Segmentation of Breast Cancer in Histopathology Images
Yunianta, A. (2026) · Computation · doi:10.3390/computation14030062 · Cited by 2
Artificial Intelligence in Oncology: Clinical Applications, Challenges, and Opportunities
Parkinson, G., Patel, R. D., Bergstrom, C. P., Vadasz, B., Amgad, M., Cooper, L. A. D., Sparano, J. A., & Lu, J. (2026) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk-26-520716 · Cited by 4

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

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

Structured crowdsourcing enables convolutional segmentation of histology images

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