Image analysis and machine learning in digital pathology: Challenges and opportunities

Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images

Sirinukunwattana, K., Raza, S. E. A., Tsang, Y.-W., Snead, D. R. J., Cree, I. A., & Rajpoot, N. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2525803 · Cited by 1,224

Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival

Beck, A. H., Sangoi, A. R., Leung, S. C. Y., Marinelli, R. J., Nielsen, T. O., van de Vijver, M. J., West, R. B., van de Rijn, M., & Koller, D. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3002564 · Cited by 780

Picture processing and psychopictorics

(1971) · Icarus · doi:10.1016/0019-1035(71)90136-9 · Cited by 766

Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential

Irshad, H., Veillard, A., Roux, L., & Racoceanu, D. (2014) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2013.2295804 · Cited by 641

Assessment of algorithms for mitosis detection in breast cancer histopathology images

Veta, M., van Diest, P. J., Willems, S. M., Wang, H., Madabhushi, A., Cruz-Roa, Á., Gonzalez, F. A., Larsen, A., Vestergaard, J. S., Dahl, A. B., et al. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.11.010 · Cited by 480

Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features

Wang, H., Roa, Á. A. C., Basavanhally, A. N., Gilmore, H. L., Shih, N. N. C., Feldman, M., Tomaszewski, J., Gonzalez, F. A., & Madabhushi, A. (2014) · Journal of medical imaging · doi:10.1117/1.jmi.1.3.034003 · Cited by 362

A Stochastic Polygons Model for Glandular Structures in Colon Histology Images

Sirinukunwattana, K., Snead, D. R. J., & Rajpoot, N. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2433900 · Cited by 257

Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks.

Ertosun, M. G., & Rubin, D. L. (2015) · PubMed · Cited by 255

Prostate cancer: local staging with endorectal surface coil MR imaging.

Schnall, M. D., Imai, Y., Tomaszewski, J., Pollack, H., Lenkinski, R. E., & Kressel, H. Y. (1991) · Radiology · doi:10.1148/radiology.178.3.1994421 · Cited by 225

Expectation–Maximization-Driven Geodesic Active Contour With Overlap Resolution (EMaGACOR): Application to Lymphocyte Segmentation on Breast Cancer Histopathology

Fatakdawala, H., Xu, J., Basavanhally, A. N., Bhanot, G., Ganesan, S., Feldman, M. D., Tomaszewski, J. E., & Madabhushi, A. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2041232 · Cited by 199

Non-small cell lung cancer diagnosis aid with histopathological images using Explainable Deep Learning techniques

Civit-Masot, J., Bañuls-Beaterio, A., Domínguez-Morales, M., Rivas-Perez, M., Muñoz-Saavedra, L., & Corral, J. M. R. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107108 · Cited by 101

Representation Learning of Histopathology Images using Graph Neural Networks

Adnan, M., Kalra, S., & Tizhoosh, H. R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00502 · Cited by 109

Hyperspectral Imaging for the Detection of Glioblastoma Tumor Cells in H&E Slides Using Convolutional Neural Networks

Ortega, S., Halicek, M. T., Fabelo, H. A., Camacho, R., Plaza, M. D. L. L., Godtliebsen, F., Callicó, G. M., & Fei, B. (2020) · Sensors · doi:10.3390/s20071911 · Cited by 95

Multidisciplinary Pattern Recognition applications: A review

Paolanti, M., & Frontoni, E. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100276 · Cited by 98

Multimodal Imaging Based on Vibrational Spectroscopies and Mass Spectrometry Imaging Applied to Biological Tissue: A Multiscale and Multiomics Review

Tuck, M., Blanc, L., Touti, R., Patterson, N. H., Nuffel, S. V., Villette, S., Taveau, J., Römpp, A., Brunelle, A., Lecomte, S., et al. (2020) · Analytical Chemistry · doi:10.1021/acs.analchem.0c04595 · Cited by 95

Faster R-CNN-Based Glomerular Detection in Multistained Human Whole Slide Images

Kawazoe, Y., Shimamoto, K., Yamaguchi, R., Shintani‐Domoto, Y., Uozaki, H., Fukayama, M., & Ohe, K. (2018) · Journal of Imaging · doi:10.3390/jimaging4070091 · Cited by 91

Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells

Kim, G., Jo, Y., Cho, H., Min, H., & Park, Y. (2018) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2018.09.068 · Cited by 86

Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association

Lujan, G. M., Quigley, J. C., Hartman, D. J., Parwani, A. V., Roehmholdt, B. F., Meter, B. V., Ardon, O., Hanna, M. G., Kelly, D., Sowards, C., et al. (2021) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_67_20 · Cited by 87

LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images

Aatresh, A. A., Alabhya, K., Lal, S., Kini, J. R., & Saxena, P. P. (2021) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-021-02410-4 · Cited by 88

Orbit image analysis machine learning software can be used for the histological quantification of acute ischemic stroke blood clots

Fitzgerald, S. T., Wang, S., Dai, D., Murphree, D. H., Pandit, A. S., Douglas, A., Rizvi, A., Kadirvel, R., Gilvarry, M., McCarthy, R., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0225841 · Cited by 84

Breast Cancer Image Classification via Multi-Network Features and Dual-Network Orthogonal Low-Rank Learning

Wang, Y., Lei, B., Elazab, A., Tan, E.-L., Wang, W., Huang, F., Gong, X., & Wang, T. (2020) · IEEE Access · doi:10.1109/access.2020.2964276 · Cited by 88

Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review

Herman, D. S., Rhoads, D. D., Schulz, W. L., & Durant, T. J. S. (2021) · Clinical Chemistry · doi:10.1093/clinchem/hvab165 · Cited by 98

Quantitative Image Analysis of Human Epidermal Growth Factor Receptor 2 Immunohistochemistry for Breast Cancer: Guideline From the College of American Pathologists

Bui, M. M., Riben, M. W., Allison, K. H., Chlipala, E. A., Colasacco, C., Kahn, A. G., Lacchetti, C., Madabhushi, A., Pantanowitz, L., Salama, M. E., et al. (2018) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2018-0378-cp · Cited by 83

Towards label-efficient automatic diagnosis and analysis: a comprehensive survey of advanced deep learning-based weakly-supervised, semi-supervised and self-supervised techniques in histopathological image analysis

Qu, L., Liu, S., Liu, X., Wang, M., & Song, Z. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac910a · Cited by 89

Myosoft: An automated muscle histology analysis tool using machine learning algorithm utilizing FIJI/ImageJ software

Encarnacion-Rivera, L., Foltz, S. J., Hartzell, H. C., & Choo, H. J. (2020) · PLoS ONE · doi:10.1371/journal.pone.0229041 · Cited by 80

Feasibility of deep learning‐based fully automated classification of microsatellite instability in tissue slides of colorectal cancer

Lee, S. H., Song, I. H., & Jang, H. (2021) · International Journal of Cancer · doi:10.1002/ijc.33599 · Cited by 82

Classification of glomerular hypercellularity using convolutional features and support vector machine

Chagas, P., Souza, L., Araújo, I., Aldeman, N. L. S., Duarte, A. A., Ângelo, M. F., dos‐Santos, W. L. C., & de Oliveira, L. R. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101808 · Cited by 82

Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities

Terranova, N., Venkatakrishnan, K., & Benincosa, L. J. (2021) · The AAPS Journal · doi:10.1208/s12248-021-00593-x · Cited by 88

Deep Learning Approaches in Histopathology

Ahmed, A. A., Abouzid, M., & Kaczmarek, E. (2022) · Cancers · doi:10.3390/cancers14215264 · Cited by 85

Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images

Fassler, D., Abousamra, S., Gupta, R., Chen, C., Zhao, M., Paredes, D., Batool, S. A., Knudsen, B. S., Escobar‐Hoyos, L. F., Shroyer, K. R., et al. (2020) · Diagnostic Pathology · doi:10.1186/s13000-020-01003-0 · Cited by 79

Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine

Kazerooni, A. F., Bagley, S. J., Akbari, H., Saxena, S., Bagheri, S., Guo, J. F., Chawla, S., Nabavizadeh, A., Mohan, S., Bakas, S., et al. (2021) · Cancers · doi:10.3390/cancers13235921 · Cited by 79

Automatic classification of tissue malignancy for breast carcinoma diagnosis

Fondón, I., Sarmiento, A., García, A. I., Silvestre, M., Eloy, C., Polónia, A., & Aguiar, P. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.03.003 · Cited by 83

Digital Microscopy, Image Analysis, and Virtual Slide Repository

Aeffner, F., Adissu, H. A., Boyle, M. C., Cardiff, R. D., Hagendorn, E., Hoenerhoff, M. J., Klopfleisch, R., Newbigging, S., Schaudien, D., Turner, O. C., et al. (2018) · ILAR Journal · doi:10.1093/ilar/ily007 · Cited by 82

Gaussian noise up-sampling is better suited than SMOTE and ADASYN for clinical decision making

Metsch, J. M., & Heider, D. (2021) · BioData Mining · doi:10.1186/s13040-021-00283-6 · Cited by 78

Global research trends and foci of artificial intelligence-based tumor pathology: a scientometric study

Shen, Z., Hu, J., Wu, H., Chen, Z., Wu, W., Lin, J., Xu, Z., Kong, J., & Lin, T. (2022) · Journal of Translational Medicine · doi:10.1186/s12967-022-03615-0 · Cited by 81

A survey on artificial intelligence in histopathology image analysis

Abdelsamea, M. M., Zidan, U., Senousy, Z., Gaber, M. M., Rakha, E. A., & Ilyas, M. (2022) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1474 · Cited by 84

ToxAIcology - The evolving role of artificial intelligence in advancing toxicology and modernizing regulatory science

Härtung, T. (2023) · ALTEX · doi:10.14573/altex.2309191 · Cited by 99

Denoising and segmentation in medical image analysis: A comprehensive review on machine learning and deep learning approaches

Kumar, R. R., & Priyadarshi, R. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19313-6 · Cited by 88

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

Ščupáková, K., Dewez, F., Walch, A. K., Heeren, R. M. A., & Balluff, B. (2020) · Angewandte Chemie International Edition · doi:10.1002/anie.202007315 · Cited by 74

Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey

Ayyad, S. M., Shehata, M., Shalaby, A. M., El‐Ghar, M. A., Ghazal, M. A., El-Melegy, M., Abdel-Hamid, N. B., Labib, L. M., Ali, H. A., & El-Baz, A. S. (2021) · Sensors · doi:10.3390/s21082586 · Cited by 76

A hybrid deep learning approach for gland segmentation in prostate histopathological images

Salvi, M., Bosco, M., Molinaro, L., Gambella, A., Papotti, M. G., Acharya, U. R., & Molinari, F. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102076 · Cited by 75

A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy

Naito, Y., Tsuneki, M., Fukushima, N., Koga, Y., Higashi, M., Notohara, K., Aishima, S., Ohike, N., Tajiri, T., Yamaguchi, H., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-87748-0 · Cited by 77

Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology—Current Affairs and Perspectives

Moscalu, M., Moscalu, R., Dascălu, C. G., Ţarcă, V., Cojocaru, E., Costin, I. M., Țarcă, E., & Șerban, I. L. (2023) · Diagnostics · doi:10.3390/diagnostics13142379 · Cited by 80

Automatic segmentation of whole-slide H&E stained breast histopathology images using a deep convolutional neural network architecture

Priego, B., Morillo, D., Fernandez-Granero, M. A., & García‐Rojo, M. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113387 · Cited by 73

Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations

Wang, X., Wang, D., Yao, Z., Xin, B., Wang, B., Lan, C., Qin, Y., Xu, S., He, D., & Liu, Y. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.01046 · Cited by 72

Amsterdam International Consensus Meeting: tumor response scoring in the pathology assessment of resected pancreatic cancer after neoadjuvant therapy

Janssen, B. V., Tutucu, F., van Roessel, S. R., Adsay, V. N., Baştürk, O., Campbell, F., Doglioni, C., Esposito, I., Feakins, R. M., Fukushima, N., et al. (2020) · Modern Pathology · doi:10.1038/s41379-020-00683-9 · Cited by 74

Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis

Liu, J., Cen, X., Yi, C., Wang, F.-A., Ding, J., Cheng, J., Wu, Q., Gai, B., Zhou, Y., He, R., et al. (2025) · Genomics, Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf011 · Cited by 75

Weakly Supervised Prostate Tma Classification Via Graph Convolutional Networks

Wang, J., Chen, R. J., Lu, M. Y., Baras, A. S., & Mahmood, F. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098534 · Cited by 69

Artificial intelligence in cancer diagnostics and therapy: current perspectives

Sen, D., & Majumder, A. (2021) · Indian Journal of Cancer · doi:10.4103/ijc.ijc_399_20 · Cited by 74

Robust nuclei segmentation in histopathology using ASPPU-Net and boundary refinement

Wan, T., Zhao, L., Feng, H., Li, D., Tong, C., & Qin, Z. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.08.103 · Cited by 68

Virtual histological staining of unlabeled autopsy tissue

Li, Y., Pillar, N., Li, J., Liu, T., Wu, D., Sun, S., Ma, G., Haan, K. D., Huang, L., Zhang, Y., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46077-2 · Cited by 67

Bone cancer detection using machine learning techniques

Shrivastava, D., Sanyal, S., Maji, A. K., & Kandar, D. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817913-0.00017-1 · Cited by 66

Single image super-resolution for whole slide image using convolutional neural networks and self-supervised color normalization

Li, B., Keikhosravi, A., Loeffler, A. G., & Eliceiri, K. W. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101938 · Cited by 68

Specific in situ inflammatory states associate with progression to renal failure in lupus nephritis

Abraham, R., Durkee, M. S., Ai, J., Veselits, M., Casella, G., Asano, Y., Chang, A., Ko, K., Oshinsky, C., Peninger, E., et al. (2022) · Journal of Clinical Investigation · doi:10.1172/jci155350 · Cited by 63

Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations

Marini, N., Marchesin, S., Otálora, S., Wodziński, M., Caputo, A., van Rijthoven, M., Aswolinskiy, W., Bokhorst, J., Podareanu, D., Petters, E., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00635-4 · Cited by 71

Artificial intelligence for phase recognition in complex laparoscopic cholecystectomy

Golany, T., Aides, A., Freedman, D., Rabani, N., Liu, Y., Rivlin, E., Corrado, G. S., Matias, Y., Khoury, W., Kashtan, H., et al. (2022) · Surgical Endoscopy · doi:10.1007/s00464-022-09405-5 · Cited by 67

A generalizable and robust deep learning algorithm for mitosis detection in multicenter breast histopathological images

Wang, X., Zhang, J., Yang, S., Xiang, J., Luo, F., Wang, M., Zhang, J., Yang, W., Huang, J., & Han, X. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102703 · Cited by 66

Weakly supervised histopathology image segmentation with self-attention

Li, K., Qian, Z., Han, Y., Chang, E., Wei, B., Lai, M., Liao, J., Fan, Y., & Xu, Y. L. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102791 · Cited by 73

Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology

Turner, O. C., Aeffner, F., Bangari, D. S., High, W., Knight, B., Forest, T., Cossic, B., Himmel, L. E., Rudmann, D. G., Bawa, B., et al. (2020) · Toxicologic Pathology · doi:10.1177/0192623319881401 · Cited by 64

Deep Learning Model as a New Trend in Computer-aided Diagnosis of Tumor Pathology for Lung Cancer

Cong, L., Feng, W., Yao, Z., Zhou, X., & Xiao, W. L. (2020) · Journal of Cancer · doi:10.7150/jca.43268 · Cited by 67

Capturing Cellular Topology in Multi-Gigapixel Pathology Images

Lu, W., Graham, S., Bilal, M., Rajpoot, N., & Minhas, F. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00138 · Cited by 63

Recognition of Cutaneous Melanoma on Digitized Histopathological Slides via Artificial Intelligence Algorithm

Logu, F. D., Ugolini, F., Maio, V., Simi, S., Cossu, A., Massi, D., Nassini, R., & Laurino, M. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01559 · Cited by 64

Learned sensing: jointly optimized microscope hardware for accurate image classification

Muthumbi, A. K., Chaware, A., Kim, K., Zhou, K. C., Konda, P. C., Chen, R. J., Judkewitz, B., Erdmann, A. A., Kappes, B., & Horstmeyer, R. W. (2019) · Biomedical Optics Express · doi:10.1364/boe.10.006351 · Cited by 61

An Image Analysis Resource for Cancer Research: PIIP—Pathology Image Informatics Platform for Visualization, Analysis, and Management

Martel, A. L., Hosseinzadeh, D., Senaras, C., Zhou, Y., Yazdanpanah, A., Shojaii, R., Patterson, E. S., Madabhushi, A., & Gurcan, M. N. (2017) · Cancer Research · doi:10.1158/0008-5472.can-17-0323 · Cited by 63

Hyperspectral imaging and deep learning for the detection of breast cancer cells in digitized histological images

Ortega, S., Halicek, M. T., Fabelo, H. A., Guerra, R., López, C., Lejeune, M., Godtliebsen, F., Callicó, G. M., & Fei, B. (2020) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2548609 · Cited by 63

Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey

Al-Thelaya, K., Gilal, N. U., Alzubaidi, M. S., Majeed, F., Agus, M., Schneider, J., & Househ, M. S. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100335 · Cited by 67

Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift

Burns, J. E., Yao, J., & Summers, R. M. (2019) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.3849 · Cited by 62

The frontier of live tissue imaging across space and time

Huang, Q., Garrett, A., Bose, S., Blocker, S. J., Rios, A. C., Clevers, H. C., & Shen, X. (2021) · Cell stem cell · doi:10.1016/j.stem.2021.02.010 · Cited by 60

Glomerulus Classification with Convolutional Neural Networks

Pedraza, A., Gallego, J., López, S. G., Gonzalez, L., Laurinavičius, A., & Bueno, G. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-60964-5_73 · Cited by 60

Assessment of a computerized quantitative quality control tool for whole slide images of kidney biopsies

Chen, Y. M., Zee, J., Smith, A. R., Jayapandian, C. P., Hodgin, J. B., Howell, D. N., Palmer, M., Thomas, D. B., Cassol, C. A., Farris, A. B., et al. (2020) · The Journal of Pathology · doi:10.1002/path.5590 · Cited by 59

Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study

Schmitt, M., Maron, R. C., Hekler, A., Stenzinger, A., Hauschild, A., Weichenthal, M., Tiemann, M., Krahl, D., Kutzner, H., Utikal, J. S., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/23436 · Cited by 63

An interpretable machine learning system for colorectal cancer diagnosis from pathology slides

Neto, P. C., Montezuma, D., Oliveira, S. P., Oliveira, D., Fraga, J., Monteiro, A. O. S., Monteiro, J., Ribeiro, L., Gonçalves, S., Reinhard, S., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00539-4 · Cited by 59

Diagnostic Accuracy of Virtual Pathology vs Traditional Microscopy in a Large Dermatopathology Study

Kent, M. N., Olsen, T. G., Feeser, T. A., Tesno, K. C., Moad, J. C., Conroy, M. P., Kendrick, M. J. J., Stephenson, S. R., Murchland, M. R., Khan, A. U., et al. (2017) · JAMA Dermatology · doi:10.1001/jamadermatol.2017.3284 · Cited by 58

A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images

Kanavati, F., Toyokawa, G., Momosaki, S., Takeoka, H., Okamoto, M., Yamazaki, K., Takeo, S., Iizuka, O., & Tsuneki, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-87644-7 · Cited by 59

Cancer diagnosis through a tandem of classifiers for digitized histopathological slides

Lichtblau, D., & Stoean, C. (2019) · PLoS ONE · doi:10.1371/journal.pone.0209274 · Cited by 58

Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers

Wong, A. N. N., He, Z., Leung, K. L., To, C. C. K., Wong, C. Y., Wong, S. C. C., Yoo, J. S., Chan, C. K. R., Chan, A. Z., Lacambra, M. D., et al. (2022) · Cancers · doi:10.3390/cancers14153780 · Cited by 64

Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence

Du, X., Chen, Z., Li, Q., Yang, S., Jiang, L., Yang, Y., Li, Y., & Gu, Z. (2023) · Bio-Design and Manufacturing · doi:10.1007/s42242-022-00226-y · Cited by 57

SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology

Zhang, J., Ma, K., Kapse, S., Saltz, J. H., Vakalopoulou, M., Prasanna, P., & Samaras, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47401-9_16 · Cited by 57

Classification and Retrieval of Digital Pathology Scans: A New Dataset

Babaie, M., Kalra, S., Sriram, A., Mitcheltree, C., Zhu, S., Khatami, A., Rahnamayan, S., & Tizhoosh, H. R. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.106 · Cited by 56

CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance

Oliveira, S. P., Neto, P. C., Fraga, J. B. D. P., Montezuma, D., Monteiro, A., Monteiro, J., Ribeiro, L., Gonçalves, S., Pinto, I., & Cardoso, J. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-93746-z · Cited by 59

Artificial intelligence and its potential in oncology

Londhe, V. Y., & Bhasin, B. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2018.10.005 · Cited by 53

DAN-NucNet: A dual attention based framework for nuclei segmentation in cancer histology images under wild clinical conditions

Ahmad, I., Xia, Y., Cui, H., & Islam, Z. U. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118945 · Cited by 54

Impact of Image Preprocessing Methods and Deep Learning Models for Classifying Histopathological Breast Cancer Images

Murcia-Gómez, D., Rojas, I., & Valenzuela, O. (2022) · Applied Sciences · doi:10.3390/app122211375 · Cited by 58

Image Analysis in Histopathology and Cytopathology: From Early Days to Current Perspectives

Mezei, T., Kolcsár, M., Joó, A., & Gurzu, S. (2024) · Journal of Imaging · doi:10.3390/jimaging10100252 · Cited by 69

Attitudes Toward Artificial Intelligence Within Dermatopathology: An International Online Survey

Polesie, S., McKee, P. H., Gardner, J. M., Gillstedt, M., Siarov, J., Neittaanmäki, N., & Paoli, J. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.591952 · Cited by 53

Integrating artificial intelligence in pathology: a qualitative interview study of users' experiences and expectations

Drogt, J., Milota, M., Vos, S., Bredenoord, A., & Jongsma, K. (2022) · Modern Pathology · doi:10.1038/s41379-022-01123-6 · Cited by 53

Journal of Social and Administrative Sciences

Kargı, B. (2015) · ADVANCED SCIENCES INDEX · Cited by 53

Multiview boosting digital pathology analysis of prostate cancer

Kwak, J. T., & Hewitt, S. M. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.02.023 · Cited by 51

Computational intelligence techniques for medical diagnosis and prognosis: Problems and current developments

Shahid, A. H., & Singh, M. P. (2019) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2019.05.010 · Cited by 51

On detection thresholds–a review on diagnostic approaches in the infectious disease laboratory and the interpretation of their results

Hahn, A., Podbielski, A., Meyer, T., Zautner, A. E., Loderstädt, U., Schwarz, N. G., Krüger, A., Cadar, D., & Frickmann, H. (2020) · Acta Tropica · doi:10.1016/j.actatropica.2020.105377 · Cited by 51

PyHIST: A Histological Image Segmentation Tool

Muñoz-Aguirre, M., Ntasis, V. F., Rojas, S., & Guigó, R. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008349 · Cited by 51

Label-free quantitative evaluation of breast tissue using Spatial Light Interference Microscopy (SLIM)

Majeed, H., Nguyen, T. H., Kandel, M. E., Kajdacsy‐Balla, A., & Popescu, G. (2018) · Scientific Reports · doi:10.1038/s41598-018-25261-7 · Cited by 50

Deep learning segmentation and quantification of Meibomian glands

Prabhu, S. M., Chakiat, A., Shashank, S. K., Vunnava, K. P., & Shetty, R. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101776 · Cited by 50

CS-CO: A Hybrid Self-Supervised Visual Representation Learning Method for H&E-stained Histopathological Images

Yang, P., Yin, X., Lu, H., Hu, Z., Zhang, X., Jiang, R., & Lv, H. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102539 · Cited by 50

An Overview of Biomedical Image Analysis From the Deep Learning Perspective

Chakraborty, S., & Mali, K. (2022) · IGI Global eBooks · doi:10.4018/978-1-6684-7544-7.ch003 · Cited by 49

AI improves accuracy, agreement and efficiency of pathologists for Ki67 assessments in breast cancer

Dy, A., Nguyen, N.-N. J., Meyer, J. M., Dawe, M., Shi, W., Androutsos, D., Fyles, A., Liu, F., Done, S. J., & Khademi, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-51723-2 · Cited by 49

Generating and evaluating synthetic data in digital pathology through diffusion models

Pozzi, M., Noei, S., Robbi, E., Cima, L., Moroni, M., Munari, E., Torresani, E., & Jurman, G. (2024) · Scientific Reports · doi:10.1038/s41598-024-79602-w · Cited by 56

GUESS: projecting machine learning scores to well-calibrated probability estimates for clinical decision-making

Schwarz, J., & Heider, D. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty984 · Cited by 54

A hybrid network for automatic hepatocellular carcinoma segmentation in H&E-stained whole slide images

Wang, X., Fang, Y., Yang, S., Zhu, D., Wang, M., Zhang, J., Tong, R. K., & Han, X. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101914 · Cited by 50

Digital pathology: semper ad meliora

Es, S. L. V. (2018) · Pathology · doi:10.1016/j.pathol.2018.10.011 · Cited by 48

Machine Learning and Artificial Intelligence in Pediatric Research: Current State, Future Prospects, and Examples in Perioperative and Critical Care

Lonsdale, H., Jalali, A., Ahumada, L. M., & Matava, C. T. (2020) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2020.02.039 · Cited by 48

Spatial prediction of permafrost occurrence in Sikkim Himalayas using logistic regression, random forests, support vector machines and neural networks

Baral, P., & Haq, M. A. (2020) · Geomorphology · doi:10.1016/j.geomorph.2020.107331 · Cited by 48

Multiplex Cellular Communities in Multi-Gigapixel Colorectal Cancer Histology Images for Tissue Phenotyping

Javed, S., Mahmood, A., Werghi, N., Benes, K., & Rajpoot, N. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3023795 · Cited by 48

A fully automated and explainable algorithm for predicting malignant transformation in oral epithelial dysplasia

Shephard, A., Bashir, R. M. S., Mahmood, H., Jahanifar, M., Minhas, F., Raza, S. E. A., McCombe, K. D., Craig, S. G., James, J. A., Brooks, J. M., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00624-8 · Cited by 56

Overdiagnosis of melanoma – causes, consequences and solutions

Kutzner, H., Jutzi, T. B., Krahl, D., Krieghoff‐Henning, E., Heppt, M. V., Hekler, A., Schmitt, M., Maron, R. C., Fröhling, S., Kalle, C. V., et al. (2020) · JDDG Journal der Deutschen Dermatologischen Gesellschaft · doi:10.1111/ddg.14233 · Cited by 49

Immune contexture analysis in immuno‐oncology: applications and challenges of multiplex fluorescent immunohistochemistry

Shakya, R., Nguyen, T. H., Waterhouse, N. J., & Khanna, R. (2020) · Clinical & Translational Immunology · doi:10.1002/cti2.1183 · Cited by 46

Deep Learning on Oral Squamous Cell Carcinoma Ex Vivo Fluorescent Confocal Microscopy Data: A Feasibility Study

Shavlokhova, V., Sandhu, S., Flechtenmacher, C., Kövesházi, I., Neumeier, F., Padrón‐Laso, V., Jonke, Ž., Saravi, B., Vollmer, M., Vollmer, A., et al. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10225326 · Cited by 47

Clinical protein science in translational medicine targeting malignant melanoma

Gil, J., Betancourt, L. H., Pla, I., Sanchez, A., Appelqvist, R., Miliotis, T., Kuras, M., Oskolas, H., Kim, Y., Horvath, Z., et al. (2019) · Cell Biology and Toxicology · doi:10.1007/s10565-019-09468-6 · Cited by 45

MitosisNet: End-to-End Mitotic Cell Detection by Multi-Task Learning

Alom, M. Z., Aspiras, T. H., Taha, T. M., Bowen, T. J., & Asari, V. K. (2020) · IEEE Access · doi:10.1109/access.2020.2983995 · Cited by 45

An interpretable decision-support model for breast cancer diagnosis using histopathology images

Krishna, S., Suganthi, S. S., Bhavsar, A. V., Yesodharan, J., & Krishnamoorthy, S. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100319 · Cited by 51

Editorial: Computational pathology for precision diagnosis, treatment, and prognosis of cancer

Cheng, J., Huang, K., & Xu, J. B. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1209666 · Cited by 4

Deep Learning Models for Digital PathologyPreprint

BenTaieb, A., & Hamarneh, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.12329 · Cited by 15

Histopathological Image Analysis: A Review

Gurcan, M. N., Boucheron, L. E., Can, A., Madabhushi, A., Rajpoot, N. M., & Yener, B. (2009) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2009.2034865 · Cited by 2,052

Machine learning in computational histopathology: Challenges and opportunities

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

Breast Cancer Detection, Segmentation and Classification on Histopathology Images Analysis: A Systematic Review

Krithiga, R., & Geetha, P. (2020) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-020-09470-w · Cited by 165