Cited by 1,093AI in cancer detectionRadiomics and Machine Learning in Medical ImagingCell Image Analysis Techniques

The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.

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

Open access

Madabhushi, Anant, Lee, George·2016·Medical Image Analysis

DOI 10.1016/j.media.2016.06.037PMC PMC5556681

Open this paper in CoChat — free account

Abstract

With the rise in whole slide scanner technology, large numbers of tissue slides are being scanned and represented and archived digitally. While digital pathology has substantial implications for telepathology, second opinions, and education there are also huge research opportunities in image computing with this new source of "big data". It is well known that there is fundamental prognostic data embedded in pathology images. The ability to mine "sub-visual" image features from digital pathology slide images, features that may not be visually discernible by a pathologist, offers the opportunity for better quantitative modeling of disease appearance and hence possibly improved prediction of disease aggressiveness and patient outcome. However the compelling opportunities in precision medicine offered by big digital pathology data come with their own set of computational challenges. Image analysis and computer assisted detection and diagnosis tools previously developed in the context of radiographic images are woefully inadequate to deal with the data density in high resolution digitized whole slide images. Additionally there has been recent substantial interest in combining and fusing radiologic imaging and proteomics and genomics based measurements with features extracted from digital pathology images for better prognostic prediction of disease aggressiveness and patient outcome. Again there is a paucity of powerful tools for combining disease specific features that manifest across multiple different length scales. The purpose of this review is to discuss developments in computational image analysis tools for predictive modeling of digital pathology images from a detection, segmentation, feature extraction, and tissue classification perspective. We discuss the emergence of new handcrafted feature approaches for improved predictive modeling of tissue appearance and also review the emergence of deep learning schemes for both object detection and tissue classification. We also briefly review some of the state of the art in fusion of radiology and pathology images and also combining digital pathology derived image measurements with molecular "omics" features for better predictive modeling. The review ends with a brief discussion of some of the technical and computational challenges to be overcome and reflects on future opportunities for the quantitation of histopathology.

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

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

Integrity signals

Registry

Crossref · 10.1016/j.media.2016.06.037

Cites

29 of 29 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.

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
Self-citation
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
Self-citation
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
Self-citation

Flagged first, then by citations

Show all 29 references

Sorted by citations

Items 201–300 of 1,000

Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning
Izadyyazdanabadi, M., Belykh, E., Mooney, M. A., Eschbacher, J. M., Nakaji, P., Yang, Y., & Preul, M. C. (2018) · Frontiers in Oncology · doi:10.3389/fonc.2018.00240 · Cited by 45
Improving Diagnosis Through Digital Pathology: Proof-of-Concept Implementation Using Smart Contracts and Decentralized File Storage
Subramanian, H., & Subramanian, S. (2022) · Journal of Medical Internet Research · doi:10.2196/34207 · Cited by 45
Multiplex Immunofluorescence and the Digital Image Analysis Workflow for Evaluation of the Tumor Immune Environment in Translational Research
Rojas, F. R., Hernandez, S. D., Lazcano, R., Laberiano-Fernández, C., & Parra, E. R. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.889886 · Cited by 45
Deep Learning Approaches and Applications in Toxicologic Histopathology: Current Status and Future Perspectives
Mehrvar, S., Himmel, L. E., Babburi, P., Goldberg, A. L., Guffroy, M., Janardhan, K. S., Krempley, A. L., & Bawa, B. (2021) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_36_21 · Cited by 46
Literature analysis of artificial intelligence in biomedicine
Hulsen, T. (2022) · Annals of Translational Medicine · doi:10.21037/atm-2022-50 · Cited by 49
The promise of graphene-based transistors for democratizing multiomics studies
Lu, H., Kane, A. A., Parkinson, J., Gao, Y., Hajian, R., Heltzen, M., Goldsmith, B. R., & Aran, K. (2021) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2021.113605 · Cited by 44
High-throughput digital pathology via a handheld, multiplexed, and AI-powered ptychographic whole slide scanner
Jiang, S., Guo, C., Song, P., Wang, T., Wang, R., Zhang, T., Wu, Q., Pandey, R., & Zheng, G. (2022) · Lab on a Chip · doi:10.1039/d2lc00084a · Cited by 44
Quantitative Histopathology of Stained Tissues using Color Spatial Light Interference Microscopy (cSLIM)
Majeed, H., Keikhosravi, A., Kandel, M. E., Nguyen, T. H., Liu, Y., Kajdacsy‐Balla, A., Tangella, K. V., Eliceiri, K. W., & Popescu, G. (2019) · Scientific Reports · doi:10.1038/s41598-019-50143-x · Cited by 42
Automated classification platform for the identification of otitis media using optical coherence tomography
Monroy, G. L., Won, J., Dsouza, R., Pande, P., Hill, M. C., Porter, R. G., Novak, M. A., Spillman, D. R., & Boppart, S. A. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0094-0 · Cited by 43
Unsupervised Machine Learning in Pathology
Roohi, A., Faust, K., Djuric, U., & Diamandis, P. (2020) · Surgical pathology clinics · doi:10.1016/j.path.2020.01.002 · Cited by 45
Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis
Chen, Y. M., Janowczyk, A. R., & Madabhushi, A. (2020) · JCO Clinical Cancer Informatics · doi:10.1200/cci.19.00068 · Cited by 43
Self-citation
Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer
Fisher, T. B., Saini, G., Rekha, T. S., Krishnamurthy, J., Bhattarai, S., Callagy, G., Webber, M., Janssen, E. A. M., Kong, J., & Aneja, R. (2024) · Breast Cancer Research · doi:10.1186/s13058-023-01752-y · Cited by 42
“Machine learning (ML) can more accurately and efficiently characterize the TME [16–18].”
Background
Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks
Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Ibraheem, A. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-59566-7 · Cited by 42
A novel machine learning approach reveals latent vascular phenotypes predictive of renal cancer outcome
Ing, N., Huang, F., Conley, A., You, S., Ma, Z., Klimov, S., Ohe, C., Yuan, X., Amin, M. B., Figlin, R. A., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-13196-4 · Cited by 42
Quantitative assessment of inflammatory infiltrates in kidney transplant biopsies using multiplex tyramide signal amplification and deep learning
Hermsen, M., Volk, V., Bräsen, J. H., Geijs, D. J., Gwinner, W., Kers, J., Linmans, J., Schaadt, N. S., Schmitz, J., Steenbergen, E. J., et al. (2021) · Laboratory Investigation · doi:10.1038/s41374-021-00601-w · Cited by 42
iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images
Neto, P. C., Oliveira, S. P., Montezuma, D., Fraga, J. B. D. P., Monteiro, A., Ribeiro, L., Gonçalves, S., Pinto, I., & Cardoso, J. S. (2022) · Cancers · doi:10.3390/cancers14102489 · Cited by 41
Quantitative Characterization of CD8+ T Cell Clustering and Spatial Heterogeneity in Solid Tumors
Gong, C., Anders, R. A., Zhu, Q., Taube, J. M., Green, B., Cheng, W., Bartelink, I. H., Vicini, P., Wang, B., & Popel, A. S. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2018.00649 · Cited by 44
Automated Quantification of Immunohistochemical Staining of Large Animal Brain Tissue Using QuPath Software
Morriss, N. J., Conley, G. M., Ospina-Mora, S., Meehan, W. P., III, Qiu, J., & Mannix, R. (2020) · Neuroscience · doi:10.1016/j.neuroscience.2020.01.006 · Cited by 44
Integrative Data Augmentation with U-Net Segmentation Masks Improves Detection of Lymph Node Metastases in Breast Cancer Patients
Jin, Y. W., Jia, S., Ashraf, A. B., & Hu, P. (2020) · Cancers · doi:10.3390/cancers12102934 · Cited by 45
Quantitative X-ray phase contrast computed tomography with grating interferometry
Birnbacher, L., Braig, E., Pfeiffer, D., Pfeiffer, F., & Herzen, J. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05259-6 · Cited by 41
Machine learning applications in imaging analysis for patients with pituitary tumors: a review of the current literature and future directions
Saha, A., Tso, S., Rabski, J., Sadeghian, A., & Cusimano, M. D. (2020) · Pituitary · doi:10.1007/s11102-019-01026-x · Cited by 41
Artificial intelligence in dermatology: A practical introduction to a paradigm shift
Eapen, B. R. (2020) · Indian Dermatology Online Journal · doi:10.4103/idoj.idoj_388_20 · Cited by 45
Low Dimensional Representation of Fisher Vectors for Microscopy Image Classification
Song, Y., Li, Q., Huang, H., Feng, D. D., Chen, M., & Cai, W. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2687466 · Cited by 40
Deep Learning for Differentiating Benign From Malignant Parotid Lesions on MR Images
Xia, X., Feng, B., Wang, J., Hua, Q., Yang, Y., Liang, S., Mou, Y., & Hu, W. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.632104 · Cited by 39
Application of digital pathology and machine learning in the liver, kidney and lung diseases
Wu, B., & Moeckel, G. W. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100184 · Cited by 42
A U-Net based framework to quantify glomerulosclerosis in digitized PAS and H&E stained human tissues
Gallego, J., Świderska-Chadaj, Ż., Markiewicz, T., Yamashita, M., Gabaldon, M.-A., & Gertych, A. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101865 · Cited by 38
Social network analysis of cell networks improves deep learning for prediction of molecular pathways and key mutations in colorectal cancer
Tajeddin, N. Z., Jahanifar, M., Bilal, M., Eastwood, M., & Rajpoot, N. (2024) · Medical Image Analysis · doi:10.1016/j.media.2023.103071 · Cited by 36
Machine Learning and Knowledge Extraction in Digital Pathology Needs an Integrative Approach
Holzinger, A., Malle, B., Kieseberg, P., Roth, P. M., Müller, H., Reihs, R., & Zatloukal, K. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-69775-8_2 · Cited by 39
Unsupervised morphological segmentation of tissue compartments in histopathological images
Fouad, S., Randell, D. A., Galton, A., Mehanna, H. M., & Landini, G. (2017) · PLoS ONE · doi:10.1371/journal.pone.0188717 · Cited by 38
Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review
Park, J. H., Kim, E. Y., Luchini, C., Eccher, A., Tizaoui, K., Shin, J. I., & Lim, B. J. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23052462 · Cited by 38
Advances in AI‐based cancer cytopathology
Yang, Y., Guan, S., Ou, Z., Li, W., Yan, L., & Situ, B. (2023) · Interdisciplinary medicine · doi:10.1002/inmd.20230013 · Cited by 37
An empirical analysis of machine learning frameworks for digital pathology in medical science
Sangeetha, S., Dhaya, R., Shah, D. T., Dharanidharan, R., & Reddy, K. P. S. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1767/1/012031 · Cited by 37
SPIE-AAPM-NCI BreastPathQ Challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment
Petrick, N., Akbar, S., Kenny, H., Nofech‐Mozes, S., Sahiner, B., Gavrielides, M. A., Kalpathy–Cramer, J., Drukker, K., & Martel, A. L. (2021) · Journal of medical imaging · doi:10.1117/1.jmi.8.3.034501 · Cited by 38
Deep learning segmentation of glomeruli on kidney donor frozen sections
Davis, R., Li, X., Xu, Y., Wang, Z., Souma, N., Sotolongo, G., Bell, J., Ellis, M., Howell, D. N., Shen, X., et al. (2021) · Journal of medical imaging · doi:10.1117/1.jmi.8.6.067501 · Cited by 37
An artificial intelligence model for the pathological diagnosis of invasion depth and histologic grade in bladder cancer
Pan, J., Hong, G., Zeng, H., Liao, C., Li, H., Yao, Y., Gan, Q., Wang, Y., Wu, S., & Lin, T. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-03888-z · Cited by 37
Automated Renal Cancer Grading Using Nuclear Pleomorphic Patterns
Holdbrook, D. A., Singh, M., Choudhury, Y., Kalaw, E., Koh, V. C. Y., Tan, H. S., Kanesvaran, R., Tan, P. H., Peng, J. Y. S., Tan, M., et al. (2018) · JCO Clinical Cancer Informatics · doi:10.1200/cci.17.00100 · Cited by 36
A deep learning model for gastric diffuse-type adenocarcinoma classification in whole slide images
Kanavati, F., & Tsuneki, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-99940-3 · Cited by 38
Histopathological Classification of Canine Cutaneous Round Cell Tumors Using Deep Learning: A Multi-Center Study
Salvi, M., Molinari, F., Iussich, S., Muscatello, L. V., Pazzini, L., Benali, S., Banco, B., Abramo, F., Maria, R. D., & Aresu, L. (2021) · Frontiers in Veterinary Science · doi:10.3389/fvets.2021.640944 · Cited by 35
An international multi-institutional validation study of the algorithm for prostate cancer detection and Gleason grading
Tolkach, Y., Ovtcharov, V., Pryalukhin, A. E., Eich, M., Gaisa, N. T., Braun, M., Radzhabov, A., Quaas, A., Hammerer, P., Dellmann, A., et al. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00424-6 · Cited by 40
Deep Learning Models for Gastric Signet Ring Cell Carcinoma Classification in Whole Slide Images
Kanavati, F., Ichihara, S., Rambeau, M., Iizuka, O., Arihiro, K., & Tsuneki, M. (2021) · Technology in Cancer Research & Treatment · doi:10.1177/15330338211027901 · Cited by 35
Histopathological Images and Multi-Omics Integration Predict Molecular Characteristics and Survival in Lung Adenocarcinoma
Chen, L., Zeng, H., Yu, X., Huang, Y., Luo, Y., & Ma, X. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.720110 · Cited by 36
Breast Invasive Ductal Carcinoma Classification on Whole Slide Images with Weakly-Supervised and Transfer Learning
Kanavati, F., & Tsuneki, M. (2021) · Cancers · doi:10.3390/cancers13215368 · Cited by 35
A deep learning model for breast ductal carcinoma in situ classification in whole slide images
Kanavati, F., Ichihara, S., & Tsuneki, M. (2022) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-021-03241-z · Cited by 34
Deep learning in digital pathology for personalized treatment plans of cancer patients
Wen, Z., Wang, S., Yang, D. M., Xie, Y., Chen, M., Bishop, J., & Xiao, G. (2023) · Seminars in Diagnostic Pathology · doi:10.1053/j.semdp.2023.02.003 · Cited by 34
A pilot study using kernelled support tensor machine for distant failure prediction in lung SBRT
Li, S., Yang, N., Li, B., Zhou, Z., Hao, H., Folkert, M. R., Iyengar, P., Westover, K. D., Choy, H., Timmerman, R. D., et al. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.09.004 · Cited by 33
Ethical issues in computational pathology
Sorell, T., Rajpoot, N., & Verrill, C. L. (2021) · Journal of Medical Ethics · doi:10.1136/medethics-2020-107024 · Cited by 35
Review of elastic light scattering from single aerosol particles and application in bioaerosol detection
Pan, Y., Aptowicz, K. B., Arnold, J. A., Cheng, S. S. M., Kalume, A., Piedra, P., Wang, C., Santarpia, J. L., & Videen, G. W. (2022) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2022.108067 · Cited by 34
Deep-Learning-Aided Detection of Mycobacteria in Pathology Specimens Increases the Sensitivity in Early Diagnosis of Pulmonary Tuberculosis Compared with Bacteriology Tests
Zaizen, Y., Kanahori, Y., Ishijima, S., Kitamura, Y., Yoon, H., Ozasa, M., Mukae, H., Bychkov, A., Hoshino, T., & Fukuoka, J. (2022) · Diagnostics · doi:10.3390/diagnostics12030709 · Cited by 34
Deep learning-based virtual H& E staining from label-free autofluorescence lifetime images
Wang, Q., Akram, A. R., Dorward, D. A., Talas, S., Monks, B., Thum, C. K., Hopgood, J. R., Javidi, M., & Vallejo, M. (2024) · npj Imaging · doi:10.1038/s44303-024-00021-7 · Cited by 33
Histopathological image and gene expression pattern analysis for predicting molecular features and prognosis of head and neck squamous cell carcinoma
Chen, L., Zeng, H., Zhang, M., Luo, Y., & Ma, X. (2021) · Cancer Medicine · doi:10.1002/cam4.3965 · Cited by 34
Artificial intelligence for solid tumour diagnosis in digital pathology
Klein, C., Zeng, Q., Arbaretaz, F., Devêvre, E., Caldéraro, J., Loménie, N., & Maiuri, M. C. (2021) · British Journal of Pharmacology · doi:10.1111/bph.15633 · Cited by 36
Robust detection and segmentation of cell nuclei in biomedical images based on a computational topology framework
Moraleda, R. R., Xiong, W., Halama, N., Breitkopf‐Heinlein, K., Dooley, S., Salinas, L., Heermann, D. W., & Valous, N. A. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.02.009 · Cited by 33
Multi-strategy ant colony optimization for multi-level image segmentation: Case study of melanoma
Zhao, D., Qi, A., Yu, F., Heidari, A. A., Chen, H., & Li, Y. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104647 · Cited by 32
A lightweight xAI approach to cervical cancer classification
Civit-Masot, J., Luna-Perejón, F., Muñoz-Saavedra, L., Domínguez-Morales, M., & Civit, A. (2024) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-024-03063-6 · Cited by 33
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
A cascade-learning approach for automated segmentation of tumour epithelium in colorectal cancer
Abdelsamea, M. M., Pitiot, A., Grineviciute, R. B., Besusparis, J., Laurinavičius, A., & Ilyas, M. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.10.030 · Cited by 32
HistoNet: A Deep Learning-Based Model of Normal Histology
Hoefling, H., Sing, T., Hossain, I., Boisclair, J., Doelemeyer, A., Flandre, T., Piaia, A., Romanet, V., Santarossa, G., Saravanan, C., et al. (2021) · Toxicologic Pathology · doi:10.1177/0192623321993425 · Cited by 32
Assessment of Immunological Features in Muscle-Invasive Bladder Cancer Prognosis Using Ensemble Learning
Gavriel, C. G., Dimitriou, N., Brieu, N., Nearchou, I. P., Arandjelović, O., Schmidt, G., Harrison, D. J., & Caie, P. D. (2021) · Cancers · doi:10.3390/cancers13071624 · Cited by 36
Automatic classification with concatenation of deep and handcrafted features of histological images for breast carcinoma diagnosis
Sethy, P. K., & Behera, S. K. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11756-5 · Cited by 31
Developmental Deconvolution for Classification of Cancer Origin
Moiso, E., Farahani, A. A., Marble, H. D., Hendricks, A., Mildrum, S., Levine, S. S., Lennerz, J. K., & Garg, S. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-1443 · Cited by 30
Gland segmentation in prostate histopathological images
Singh, M., Kalaw, E., Giron, D. M., Chong, K.-T., Tan, C. L., & Lee, H. K. (2017) · Journal of medical imaging · doi:10.1117/1.jmi.4.2.027501 · Cited by 32
A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning
Tsuneki, M., Abe, M., & Kanavati, F. (2022) · Diagnostics · doi:10.3390/diagnostics12030768 · Cited by 31
Convolutional neural network assistance significantly improves dermatologists’ diagnosis of cutaneous tumours using clinical images
Ba, W., Wu, H., Chen, W. W., Wang, S. H., Zhang, Z. Y., Wei, X. J., Wang, W. J., Yang, L., Zhou, D., Zhuang, Y., et al. (2022) · European Journal of Cancer · doi:10.1016/j.ejca.2022.04.015 · Cited by 32
Fusing hand-crafted and deep-learning features in a convolutional neural network model to identify prostate cancer in pathology images
Huang, X., Li, Z., Zhang, M., & Gao, S. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.994950 · Cited by 31
Machine Learning Meets Cancer
Varlamova, E. V., Butakova, M. A., Semyonova, V. V., Soldatov, S. A., Poltavskiy, A. V., Kit, O. I., & Soldatov, A. V. (2024) · Cancers · doi:10.3390/cancers16061100 · Cited by 31
Tissue Phenomics for prognostic biomarker discovery in low- and intermediate-risk prostate cancer
Harder, N., Athelogou, M., Hessel, H., Brieu, N., Yiğitsoy, M., Zimmermann, J., Baatz, M., Büchner, A., Stief, C. G., Kirchner, T., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-22564-7 · Cited by 31
Joint categorical and ordinal learning for cancer grading in pathology images
Vuong, T. T. L., Kim, K., Song, B., & Kwak, J. T. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102206 · Cited by 34
Progress on deep learning in digital pathology of breast cancer: a narrative review
Zhu, J., Liu, M., & Li, X. (2022) · Gland Surgery · doi:10.21037/gs-22-11 · Cited by 33
A review of the current status and progress in difficult airway assessment research
Chen, H., Zheng, Y., Fu, Q., & Li, P. (2024) · European journal of medical research · doi:10.1186/s40001-024-01759-x · Cited by 35
Patch-level Tumor Classification in Digital Histopathology Images with Domain Adapted Deep Learning
Xia, T., Kumar, A., Feng, D. D., & Kim, J. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512353 · Cited by 30
Enhancing the Value of Histopathological Assessment of Allograft Biopsy Monitoring
Wood, M. A., Lesniak, A. J., & Demetris, A. J. (2019) · Transplantation · doi:10.1097/tp.0000000000002656 · Cited by 29
Weakly-Supervised Classification of HER2 Expression in Breast Cancer Haematoxylin and Eosin Stained Slides
Oliveira, S. P., Pinto, J. R., Gonçalves, T., Canas‐Marques, R., Cardoso, M. J., Oliveira, H. P., & Cardoso, J. S. (2020) · Applied Sciences · doi:10.3390/app10144728 · Cited by 29
Development and deployment of a histopathology-based deep learning algorithm for patient prescreening in a clinical trial
Ramon, A. J., Parmar, C., Carrasco‐Zevallos, O. M., Csiszer, C., Yip, S. S. F., Raciti, P., Stone, N. L., Triantos, S., Quiroz, M. M., Crowley, P., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49153-9 · Cited by 28
Deep learning for damaged tissue detection and segmentation in Ki-67 brain tumor specimens based on the U-net model
Świderska-Chadaj, Ż., Markiewicz, T., Gallego, J., Bueno, G., Grala, B., & Lorent, M. (2018) · Bulletin of the Polish Academy of Sciences Technical Sciences · doi:10.24425/bpas.2018.125932 · Cited by 29
Super-resolution recurrent convolutional neural networks for learning with multi-resolution whole slide images
Mukherjee, L., Bui, H. D., Keikhosravi, A., Loeffler, A. G., & Eliceiri, K. W. (2019) · Journal of Biomedical Optics · doi:10.1117/1.jbo.24.12.126003 · Cited by 30
Survival prediction of stomach cancer using expression data and deep learning models with histopathological images
Wei, T., Yuan, X., Gao, R., Johnston, L. J., Zhou, J. N., Wang, Y., Kong, W., Xie, Y., Zhang, Y., Xu, D., et al. (2022) · Cancer Science · doi:10.1111/cas.15592 · Cited by 30
Revolutionizing Medical Practice: The Impact of Artificial Intelligence (AI) on Healthcare
(2024) · Open Access Journal of Applied Science and Technology · doi:10.33140/oajast.02.01.07 · Cited by 31
Advanced Morphologic Analysis for Diagnosing Allograft Rejection
Peyster, E. G., Madabhushi, A., & Margulies, K. B. (2018) · Transplantation · doi:10.1097/tp.0000000000002189 · Cited by 28
Self-citation
Ensembling Neural Networks for Digital Pathology Images Classification and Segmentation
Pimkin, A., Makarchuk, G., Кондратенко, В., Pisov, M., Krivov, E., & Belyaev, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93000-8_100 · Cited by 29
Deep Boltzmann machine algorithm for accurate medical image analysis for classification of cancerous region
Jeyaraj, P. R., & Nadar, E. R. S. (2019) · Cognitive Computation and Systems · doi:10.1049/ccs.2019.0004 · Cited by 31
Current opinion, status and future development of digital pathology in Switzerland
Unternaehrer, J., Grobholz, R., Janowczyk, A. R., & Zlobec, I. (2019) · Journal of Clinical Pathology · doi:10.1136/jclinpath-2019-206155 · Cited by 28
Acceleration of autofocusing with improved edge extraction using structure tensor and Schatten norm
Ren, Z., Lam, E. Y. M., & Zhao, J. (2020) · Optics Express · doi:10.1364/oe.392544 · Cited by 29
Generating Region of Interests for Invasive Breast Cancer in Histopathological Whole-Slide-Image
Patil, S. M., Li, T., & Wang, M. D. (2020) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac48688.2020.0-174 · Cited by 28
Deep Learning Models for Poorly Differentiated Colorectal Adenocarcinoma Classification in Whole Slide Images Using Transfer Learning
Tsuneki, M., & Kanavati, F. (2021) · Diagnostics · doi:10.3390/diagnostics11112074 · Cited by 26
Lateral lymph node dissection for low rectal cancer: Is it necessary?
Christou, N., Meyer, J., Toso, C., Ris, F., & Buchs, N. C. (2019) · World Journal of Gastroenterology · doi:10.3748/wjg.v25.i31.4294 · Cited by 28
Same same but different: A Web‐based deep learning application revealed classifying features for the histopathologic distinction of cortical malformations
Kubach, J., Mühlebner, A., Söylemezoğlu, F., Miyata, H., Niehusmann, P., Honavar, M., Rogério, F., Kim, S. H., Aronica, E., Garbelli, R., et al. (2020) · Epilepsia · doi:10.1111/epi.16447 · Cited by 33
Virtual birefringence imaging and histological staining of amyloid deposits in label-free tissue using autofluorescence microscopy and deep learning
Yang, X., Bai, B., Zhang, Y., Aydın, M., Li, Y., Selçuk, Ş. Y., Costa, P. C., Guo, Z., Fishbein, G. A., Atlan, K., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52263-z · Cited by 29
Intelligence artificielle : quel avenir en anatomie pathologique ?
Zemouri, R. M., Devalland, C., Valmary‐Degano, S., & Noureddine Zerhouni, A. (2019) · Annales de Pathologie · doi:10.1016/j.annpat.2019.01.004 · Cited by 27
A Multiple-Instance Learning-Based Convolutional Neural Network Model to Detect the IDH1 Mutation in the Histopathology Images of Glioma Tissues
Cui, D.-N., Liu, Y., Liu, G., & Liu, L. (2020) · Journal of Computational Biology · doi:10.1089/cmb.2019.0410 · Cited by 27
An Ensemble-based Active Learning for Breast Cancer Classification
Lee, S., Amgad, M. T., Masoud, M., Subramanian, R., Gutman, D. A., & Cooper, L. (2019) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8983317 · Cited by 27
An Overview of Biomedical Image Analysis From the Deep Learning Perspective
Chakraborty, S., & Mali, K. (2020) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-2736-8.ch008 · Cited by 27
Histopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies
Irshaid, L., Bleiberg, J., Weinberger, E., Garritano, J., Shallis, R. M., Patsenker, J., Lindenbaum, O., Kluger, Y., Katz, S. G., & Xu, M. L. (2021) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2020-0510-oa · Cited by 28
Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach
Jang, H., Lee, A., Kang, J. K., Song, I. H., & Lee, S. H. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i44.7687 · Cited by 29
Survival prediction on intrahepatic cholangiocarcinoma with histomorphological analysis on the whole slide images
Xie, J., Pu, X., He, J., Qiu, Y., Lu, C. Y., Gao, W., Wang, X., Lu, H., Shi, J., Xu, Y., et al. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105520 · Cited by 27
Self-citation
Pathological prognosis classification of patients with neuroblastoma using computational pathology analysis
Liu, Y., Jia, Y., Hou, C., Li, N., Zhang, N., Yan, X., Yang, L., Guo, Y. X., Chen, H., Li, J., et al. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105980 · Cited by 27
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
Continual learning in medical image analysis: A comprehensive review of recent advancements and future prospects
Kumari, P., Chauhan, J., Bozorgpour, A., Huang, B., Azad, R., & Merhof, D. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103730 · Cited by 46
A New Classification of Benign, Premalignant, and Malignant Endometrial Tissues Using Machine Learning Applied to 1413 Candidate Variables
Downing, M. J., Papke, D. J., Tyekucheva, S., & Mutter, G. L. (2019) · International Journal of Gynecological Pathology · doi:10.1097/pgp.0000000000000615 · Cited by 26
Rectified Cross-Entropy and Upper Transition Loss for Weakly Supervised Whole Slide Image Classifier
Chen, H., Han, X., Fan, X., Lou, X., Liu, H., Huang, J., & Yao, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32239-7_39 · Cited by 26
Automated histologic diagnosis of CNS tumors with machine learning
Khalsa, S. S. S., Hollon, T. C., Adapa, A. R., Urias, E., Srinivasan, S., Jairath, N. K., Szczepanski, J. M., Ouillette, P. D., Camelo-Piragua, S. I., & Orringer, D. A. (2020) · CNS Oncology · doi:10.2217/cns-2020-0003 · Cited by 28

Authors: Madabhushi, A., & Lee, G.
Affiliations: Case Western Reserve University (US)
Corresponding author: Madabhushi, Anant
ORCID: Madabhushi, Anant
Lee, George
Venue: Medical Image Analysis
Publisher: Elsevier BV
Published: 2016-10
Volume: 33
Pages: 170-175
ISSN: 1361-8415 (ISSN-L)
DOI: 10.1016/j.media.2016.06.037
PMC: PMC5556681
OpenAlex: W2470965540
Cited by: 1,093
References: 29
Citations by year: 2026: 132 · 2025: 140 · 2024: 128 · 2023: 114 · 2022: 122 · 2021: 131 · 2020: 140 · 2019: 94 · 2018: 58 · 2017: 32 · 2016: 1
FWCI: 50.69
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (am, 319 d)
Locations: Medical Image Analysis (acceptedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Cancer Institute of the National Institutes of Health (1U24CA199374, 1R01CA202752, R21CA179327, R21CA195152, K01ES026841)
National Institute of Diabetes and Digestive and Kidney Diseases (R01DK098503)
DOD Prostate Cancer Synergistic Idea Development Award (PC120857)
DOD Lung Cancer Idea Development New Investigator Award (LC130463)
DOD Prostate Cancer Idea Development Award (PCRP W81XWH-15-1-0558)
Case Comprehensive Cancer Center Pilot Grant
Cleveland Clinic
Case Western Reserve University
Topics: Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
Keywords: digital pathology, whole slide images, image analysis, machine learning, deep learning, object detection, tissue classification, image segmentation, feature extraction, handcrafted features, multi-omics integration, outcome prediction, precision medicine, telepathology
MeSH terms: Machine Learning, Humans, Image Processing, Computer-Assisted, Pathology, Radiology, Telepathology, Precision Medicine

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

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

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