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

Digital twin concept: Healthcare, education, research

Peshkova, M. A., Alekseevna, Y. V., Rudenko, E., Kretova, N. V., Timashev, P. S., & Демура, Т. А. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100313 · Cited by 26

Computational pathology improves risk stratification of a multi-gene assay for early stage ER+ breast cancer

Chen, Y., Li, H., Janowczyk, A. R., Toro, P., Corredor, G., Whitney, J. R., Lu, C. Y., Koyuncu, C., Mokhtari, M., Buzzy, C. A., et al. (2023) · npj Breast Cancer · doi:10.1038/s41523-023-00545-y · Cited by 26

Clinical knowledge-guided hybrid classification network for automatic periodontal disease diagnosis in X-ray image

Mei, L., Deng, K., Cui, Z., Fang, Y., Li, Y., Lai, H., Tonetti, M. S., & Shen, D. G. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103376 · Cited by 25

Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification

Lin, C., & Jeng, S.-Y. (2020) · Diagnostics · doi:10.3390/diagnostics10090662 · Cited by 25

A Deep CNN Technique for Detection of Breast Cancer Using Histopathology Images

Wadhwa, G., & Kaur, A. (2020) · Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) · doi:10.1109/accthpa49271.2020.9213192 · Cited by 25

An Efficient Lightweight CNN and Ensemble Machine Learning Classification of Prostate Tissue Using Multilevel Feature Analysis

Bhattacharjee, S., Kim, C., Prakash, D., Park, H., Cho, N.-H., & Choi, H. (2020) · Applied Sciences · doi:10.3390/app10228013 · Cited by 29

In Situ Classification of Cell Types in Human Kidney Tissue Using 3D Nuclear Staining

Woloshuk, A., Khochare, S., Almulhim, A., McNutt, A. T., Dean, D. F., Barwinska, D., Ferkowicz, M. J., Eadon, M. T., Kelly, K. J., Dunn, K. W., et al. (2020) · Cytometry Part A · doi:10.1002/cyto.a.24274 · Cited by 25

Reliable computational quantification of liver fibrosis is compromised by inherent staining variation

Astbury, S., Grove, J. I., Dorward, D. A., Guha, I. N., Fallowfield, J. A., & Kendall, T. J. (2021) · The Journal of Pathology Clinical Research · doi:10.1002/cjp2.227 · Cited by 24

Scope2Screen: Focus+Context Techniques for Pathology Tumor Assessment in Multivariate Image Data

Jessup, J., Krueger, R., Warchol, S., Hoffer, J., Muhlich, J. L., Ritch, C. C., Gaglia, G., Coy, S., Chen, Y.-A., Lin, J., et al. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114786 · Cited by 25

Immunohistochemistry Microarrays

Li, H., Brewer, G., Ongo, G., Normandeau, F., Ömeroğlu, A., & Juncker, D. (2017) · Analytical Chemistry · doi:10.1021/acs.analchem.7b00807 · Cited by 25

CWC-transformer: a visual transformer approach for compressed whole slide image classification

Wang, Y., Guo, J. M., Yang, Y., Kang, Y., Xia, Y., Li, Z., Duan, Y., & Wang, K. (2023) · Neural Computing and Applications · doi:10.1007/s00521-022-07857-3 · Cited by 29

Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram

Chen, D., Lai, J., Cheng, J., Fu, M., Lin, L., Chen, F., Huang, R. H., Chen, J., Lu, J., Chen, Y., et al. (2023) · iScience · doi:10.1016/j.isci.2023.106246 · Cited by 23

A Weakly Supervised Deep Learning Model and Human–Machine Fusion for Accurate Grading of Renal Cell Carcinoma from Histopathology Slides

Zheng, Q., Yang, R., Xu, H., Fan, J., Jiao, P., Ni, X., Yuan, J., Wang, L., Chen, Z., & Liu, X. (2023) · Cancers · doi:10.3390/cancers15123198 · Cited by 26

Dig information of nanogenerators by machine learning

Zhang, J., Yu, Y., Zhang, L., Chen, J., Wang, X., & Wang, X. (2023) · Nano Energy · doi:10.1016/j.nanoen.2023.108656 · Cited by 27

Recent advances of pathomics in colorectal cancer diagnosis and prognosis

Wu, Y., Li, Y., Xiong, X., Liu, X., Lin, B., & Xu, B. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1094869 · Cited by 27

Applications of machine learning in time-domain fluorescence lifetime imaging: a review

Gouzou, D., Taimori, A., Haloubi, T., Finlayson, N., Wang, Q., Hopgood, J. R., & Vallejo, M. (2023) · Methods and Applications in Fluorescence · doi:10.1088/2050-6120/ad12f7 · Cited by 26

Deep Recurrent Attention Models for Histopathological Image AnalysisPreprint

Momeni, A., Thibault, M., & Gevaert, O. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/438341 · Cited by 26

Artificial intelligence in dermatopathology: Updates, strengths, and challenges

Cazzato, G., & Rongioletti, F. (2024) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2024.06.010 · Cited by 24

Enhancing diagnosis of Hirschsprung’s disease using deep learning from histological sections of post pull-through specimens: preliminary results

Duci, M., Magoni, A., Santoro, L., Tos, A. P. D., Gamba, P., Uccheddu, F., & Leon, F. F. (2023) · Pediatric Surgery International · doi:10.1007/s00383-023-05590-z · Cited by 24

Artificial Intelligence in Lymphoma Histopathology: Systematic Review

Fu, Y., Huang, Z., Deng, X., Xu, L., Liu, Y., Zhang, M., Liu, J., & Huang, B. (2025) · Journal of Medical Internet Research · doi:10.2196/62851 · Cited by 27

Staged Detection–Identification Framework for Cell Nuclei in Histopathology Images

Li, X., Li, W., & Tao, R. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2894044 · Cited by 24

Computer-Aided Bacillus Detection in Whole-Slide Pathological Images Using a Deep Convolutional Neural Network

Lo, C., Wu, Y., Li, Y., & Lee, C.-C. (2020) · Applied Sciences · doi:10.3390/app10124059 · Cited by 24

Sampling Based Tumor Recognition in Whole-Slide Histology Image With Deep Learning Approaches

Shen, Y., & Ke, J. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3062230 · Cited by 25

Evaluating Very Deep Convolutional Neural Networks for Nucleus Segmentation from Brightfield Cell Microscopy Images

Ali, M. A. S., Misko, O., Salumaa, S., Papkov, M., Palo, K., Fishman, D., & Parts, L. (2021) · SLAS DISCOVERY · doi:10.1177/24725552211023214 · Cited by 25

A loss-based patch label denoising method for improving whole-slide image analysis using a convolutional neural network

Ashraf, M., Robles, W. R. Q., Kim, M., Ko, Y. S., & Yi, M. Y. (2022) · Scientific Reports · doi:10.1038/s41598-022-05001-8 · Cited by 26

Multi stain graph fusion for multimodal integration in pathology

Dwivedi, C., Nofallah, S., Pouryahya, M., Iyer, J. S., Leidal, K., Chung, C., Watkins, T. R., Billin, A. N., Myers, R. P., Abel, J., et al. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00200 · Cited by 24

Clinical Relevance of Computationally Derived Attributes of Peritubular Capillaries from Kidney Biopsies

Chen, Y. M., Zee, J., Janowczyk, A. R., Rubin, J., Toro, P., Lafata, K. J., Mariani, L. H., Holzman, L. B., Hodgin, J. B., Madabhushi, A., et al. (2023) · Kidney360 · doi:10.34067/kid.0000000000000116 · Cited by 24

Advances in the computational and molecular understanding of the prostate cancer cell nucleus

Carleton, N. M., Lee, G., Madabhushi, A., & Veltri, R. W. (2018) · Journal of Cellular Biochemistry · doi:10.1002/jcb.27156 · Cited by 24

A whole-slide image grading benchmark and tissue classification for cervical cancer precursor lesions with inter-observer variability

Albayrak, A., Akhan, A. Ü., Çalık, N., Çapar, A., Bilgin, G., Töreyın, B. U., Müezzínoğlu, B., Türkmen, İ., & Durak-Ata, L. (2021) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-021-02388-w · Cited by 24

Prediction Model for the Healthcare Industry Using Machine Learning

Saraswat, B. K., Saxena, A. K., & Vashist, P. C. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-6906-7_4 · Cited by 23

Impact of scanner variability on lymph node segmentation in computational pathology

Khan, A., Janowczyk, A. R., Müller, F. C., Blank, A., Nguyen, H. G., Abbet, C., Studer, L., Lugli, A., Dawson, H., Thiran, J., et al. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100127 · Cited by 23

A digital pathology workflow for the segmentation and classification of gastric glands: Study of gastric atrophy and intestinal metaplasia cases

Barmpoutis, P., Waddingham, W., Yuan, J., Ross, C., Kayhanian, H., Stathaki, T., Alexander, D. C., & Jansen, M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0275232 · Cited by 23

Standardized Classification of Lung Adenocarcinoma Subtypes and Improvement of Grading Assessment Through Deep Learning

Lami, K., Ota, N., Yamaoka, S., Bychkov, A., Matsumoto, K., Uegami, W., Munkhdelger, J., Seki, K., Sukhbaatar, O., Attanoos, R. L., et al. (2023) · American Journal Of Pathology · doi:10.1016/j.ajpath.2023.07.002 · Cited by 22

Impact of H&E Stain Normalization on Deep Learning Models in Cancer Image Classification: Performance, Complexity, and Trade-Offs

Madusanka, N., Jayalath, P., Fernando, D., Yasakethu, S. L. P., & Lee, B.-I. (2023) · Cancers · doi:10.3390/cancers15164144 · Cited by 23

Histopathologic Image Processing: A ReviewPreprint

de Matos, J., Britto, A. D. S., Oliveira, L. S., & Koerich, A. L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.07900 · Cited by 33

Digital pathology imaging and computer-aided diagnostics as a novel tool for standardization of evaluation of aganglionic megacolon (Hirschsprung disease) histopathology

Schilling, F., Geppert, C. E., Strehl, J. D., Hartmann, A., Küerten, S., Brehmer, A., & Jabari, S. (2018) · Cell and Tissue Research · doi:10.1007/s00441-018-2911-1 · Cited by 22

Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images

Xing, F., Cornish, T. C., Bennett, T. D., & Ghosh, D. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3042789 · Cited by 23

Feature fusion of Raman chemical imaging and digital histopathology using machine learning for prostate cancer detection

Doherty, T., McKeever, S., Al-Attar, N., Watson-Murphy, T., Aura, C. M., Rahman, A., O’Neill, A., Finn, S. P., Kay, E. W., Gallagher, W. M., et al. (2021) · The Analyst · doi:10.1039/d1an00075f · Cited by 22

Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects

Wang, Y., Wen, Q., Jin, L., & Chen, W. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11164918 · Cited by 24

AI-based multi-PRS models outperform classical single-PRS models

Klau, J. H., Maj, C., Klinkhammer, H., Krawitz, P. M., Mayr, A. J., Hillmer, A. M., Schumacher, J., & Heider, D. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1217860 · Cited by 23

An indeterminacy fusion of encoder-decoder network based on neutrosophic set for white blood cells segmentation

Guo, Y., Shahin, A. I., & Garg, H. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123156 · Cited by 25

An End-to-end System for Automatic Characterization of Iba1 Immunopositive Microglia in Whole Slide Imaging

Kyriazis, A. D., Noroozizadeh, S., Refaee, A., Choi, W., Chu, L.-T., Bashir, A., Cheng, W. H., Zhao, R., Namjoshi, D., Salcudean, S. E., et al. (2018) · Neuroinformatics · doi:10.1007/s12021-018-9405-x · Cited by 20

A Comparative Evaluation of Texture Features for Semantic Segmentation of Breast Histopathological Images

Rashmi, R., Prasad, K., Udupa, C. B. K., & Shwetha, V. (2020) · IEEE Access · doi:10.1109/access.2020.2984522 · Cited by 22

Tensor analysis with n-mode generalized difference subspace

Gatto, B. B., Santos, E. M. D., Koerich, A. L., Fukui, K., & Júnior, W. S. S. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114559 · Cited by 22

AI Deployment on GBM Diagnosis: A Novel Approach to Analyze Histopathological Images Using Image Feature-Based Analysis

Cheung, E. Y., Wu, R. W. K., Li, A. S. M., & Chu, E. S. M. (2023) · Cancers · doi:10.3390/cancers15205063 · Cited by 21

Application of deep learning-based multimodal fusion technology in cancer diagnosis: A survey

Li, Y., Pan, L., Peng, Y., Li, X., Wang, X., Qu, L., Song, Q., Liang, Q., & Peng, S. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109972 · Cited by 25

Automatic Diagnostic Tool for Predicting Cancer Grade in Bladder Cancer Patients Using Deep Learning

Wetteland, R., Kvikstad, V., Eftestøl, T., Tøssebro, E., Lillesand, M., Janssen, E. A. M., & Engan, K. (2021) · IEEE Access · doi:10.1109/access.2021.3104724 · Cited by 21

Spatial analysis of histology in 3D: quantification and visualization of organ and tumor level tissue environment

Ruusuvuori, P., Valkonen, M., Kartasalo, K., Valkonen, M., Visakorpi, T., Nykter, M., & Latonen, L. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e08762 · Cited by 28

Machine learning classification of placental villous infarction, perivillous fibrin deposition, and intervillous thrombus

Goldstein, J. A., Nateghi, R., Irmakci, I., & Cooper, L. (2023) · Placenta · doi:10.1016/j.placenta.2023.03.003 · Cited by 20

ki67 nuclei detection and ki67-index estimation: a novel automatic approach based on human vision modeling

Barricelli, B., Casiraghi, E., Gliozzo, J., Huber, V., Leone, B., Rizzi, A., & Vergani, B. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3285-4 · Cited by 22

Artificial intelligence and radiomics in pediatric molecular imaging

Wagner, M. W., Bilbily, A., Beheshti, M., Shammas, A., & Vali, R. (2020) · Methods · doi:10.1016/j.ymeth.2020.06.008 · Cited by 21

Virtual staining for pathology: Challenges, limitations and perspectives

Lin, W., Hu, Y., Zhu, R., Wang, B., & Wang, L. (2025) · Intelligent Oncology · doi:10.1016/j.intonc.2025.03.005 · Cited by 23

Predicting lung cancer bone metastasis using CT and pathological imaging with a Swin Transformer model

Li, W., Zou, X., Zhang, J., Hu, M., Chen, G., & Su, S. (2025) · Journal of bone oncology · doi:10.1016/j.jbo.2025.100681 · Cited by 20

Digital pathology in the diagnostic setting: beyond technology into best practice and service management

Cheng, C. L., & Tan, P. H. (2017) · Journal of Clinical Pathology · doi:10.1136/jclinpath-2016-204272 · Cited by 20

Optimum web viewer application for DICOM whole slide image visualization in anatomical pathology

Lajara, N., Espinosa-Aranda, J. L., Déniz, Ó., & Bueno, G. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.104983 · Cited by 20

InShaDe: Invariant Shape Descriptors for visual 2D and 3D cellular and nuclear shape analysis and classification

Al-Thelaya, K., Agus, M., Gilal, N. U., Yang, Y., Pintore, G., Gobbetti, E., Calì, C., Magistretti, P. J., Mifsud, W., & Schneider, J. (2021) · Computers & Graphics · doi:10.1016/j.cag.2021.04.037 · Cited by 20

Region Segmentation of Whole-Slide Images for Analyzing Histological Differentiation of Prostate Adenocarcinoma Using Ensemble EfficientNetB2 U-Net with Transfer Learning Mechanism

Ugli, I. K. K., Bhattacharjee, S., Sumon, R. I., Hwang, Y.-B., Rahman, H., Lee, M.-J., Kim, H., Park, E., Cho, N.-H., & Choi, H. (2023) · Cancers · doi:10.3390/cancers15030762 · Cited by 21

Digital Pathology Ecosystem: Basic Elements to Revolutionize the Diagnosis and Monitoring of Diseases in Health Sector

Coccia, M. (2024) · Contributions to management science · doi:10.1007/978-3-031-58359-9_5 · Cited by 22

SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology

Kapse, S., Pati, P., Das, S., Zhang, J., Chen, C., Vakalopoulou, M., Saltz, J. H., Samaras, D., Gupta, R., & Prasanna, 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/cvpr52733.2024.01067 · Cited by 24

Whole-Section Tumor Micro-Architecture Analysis by a Two-Dimensional Phasor-Based Approach Applied to Polarization-Dependent Second Harmonic Imaging

Scodellaro, R., Bouzin, M., Mingozzi, F., D’Alfonso, L., Granucci, F., Collini, M., Chirico, G. G., & Sironi, L. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2019.00527 · Cited by 20

Exascale Deep Learning to Accelerate Cancer Research

Patton, R. M., Johnston, T., Young, S. R., Schuman, C. D., Potok, T. E., Rose, D. C., Lim, S.-H., Chae, J., Hou, L., Abousamra, S., et al. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006467 · Cited by 20

Feasibility of fully automated classification of whole slide images based on deep learning

Cho, K., Lee, S. H., & Jang, H. (2020) · Korean Journal of Physiology and Pharmacology · doi:10.4196/kjpp.2020.24.1.89 · Cited by 20

Academics as leaders in the cancer artificial intelligence revolution

Kochanny, S. E., & Pearson, A. T. (2020) · Cancer · doi:10.1002/cncr.33284 · Cited by 20

Developing a Qualification and Verification Strategy for Digital Tissue Image Analysis in Toxicological Pathology

Żuraw, A., Staup, M. A., Klopfleisch, R., Aeffner, F., Brown, D. L., Westerling, T., & Rudmann, D. G. (2020) · Toxicologic Pathology · doi:10.1177/0192623320980310 · Cited by 20

Can Contrast-Enhanced Ultrasound Increase or Predict the Success Rate of Testicular Sperm Aspiration in Patients With Azoospermia?

Xue, H., Wang, S.-Y., Cui, L., & Hong, K. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.20436 · Cited by 20

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

Sendra, A. L., Heras, A. D. L., Ávila-Gutiérrez, M. J., & Zamora‐Polo, F. (2020) · Sensors · doi:10.3390/s20061553 · Cited by 20

Risks and benefits of dermatological machine learning health care applications—an overview and ethical analysis

Willem, T., Krammer, S., Böhm, A. ‐., French, L. E., Hartmann, D., Lasser, T., & Buyx, A. (2022) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.18192 · Cited by 20

Unsupervised super-resolution reconstruction of hyperspectral histology images for whole-slide imaging

Ma, L., Rathgeb, A. P., Mubarak, H. K., Tran, M. H., & Fei, B. (2022) · Journal of Biomedical Optics · doi:10.1117/1.jbo.27.5.056502 · Cited by 19

Nanotechnology and machine learning enable circulating tumor cells as a reliable biomarker for radiotherapy responses of gastrointestinal cancer patients

Poellmann, M. J., Bu, J., Liu, S. K., Wang, A. Z., Seyedin, S. N., Chandrasekharan, C., Hong, H., Kim, Y. S., Caster, J. M., & Hong, S. (2023) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2023.115117 · Cited by 20

Tissue spatial correlation as cancer marker

Takabayashi, M., Majeed, H., Kajdacsy‐Balla, A., & Popescu, G. (2019) · Journal of Biomedical Optics · doi:10.1117/1.jbo.24.1.016502 · Cited by 19

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 · doi:10.1002/ange.202007315 · Cited by 19

Artificial intelligence: its role in dermatopathology

Jartarkar, S. R. (2022) · Indian Journal of Dermatology Venereology and Leprology · doi:10.25259/ijdvl_725_2021 · Cited by 18

Multi-omics synergy in oncology: Unraveling the complex interplay of radiomic, genoproteomic, and pathological data

Luo, Y., Li, Y., Fang, M., Wang, S., Shao, L., Zou, R., Dong, D., Liu, Z., Wei, J., & Tian, J. (2024) · Intelligent Oncology · doi:10.1016/j.intonc.2024.10.003 · Cited by 18

Ovarian Toxicity Assessment in Histopathological Images Using Deep Learning

Hu, F., Schutt, L., Kozlowski, C., Regan, K. S., Dybdal, N. O., & Schutten, M. M. (2019) · Toxicologic Pathology · doi:10.1177/0192623319877871 · Cited by 18

Bioinformatics analysis of whole slide images reveals significant neighborhood preferences of tumor cells in Hodgkin lymphoma

Hannig, J., Schäfer, H., Ackermann, J., Hebel, M., Schäfer, T., Döring, C., Hartmann, S., Hansmann, M., & Koch, I. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007516 · Cited by 18

High-Speed Piezoresponse Force Microscopy and Machine Learning Approaches for Dynamic Domain Growth in Ferroelectric Materials

Liu, Y., Yu, B., Liu, Z., Beck, D. E., & Zeng, K. (2020) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b21306 · Cited by 18

Pathomics in urology

Schuettfort, V. M., Pradère, B., Rink, M., Compérat, É., & Shariat, S. F. (2020) · Current Opinion in Urology · doi:10.1097/mou.0000000000000813 · Cited by 18

A Holistic Appraisal of Stromal Differentiation in Colorectal Cancer: Biology, Histopathology, Computation, and Genomics

Hacking, S. M., Chakraborty, B., Nasim, R., Vitkovski, T., & Thomas, R. (2021) · Pathology - Research and Practice · doi:10.1016/j.prp.2021.153378 · Cited by 18

Machine Learning for The Prediction of Ranked Applicants and Matriculants to an Internal Medicine Residency Program

Rees, C. A., & Ryder, H. F. (2022) · Teaching and Learning in Medicine · doi:10.1080/10401334.2022.2059664 · Cited by 18

Shortcomings and areas for improvement in digital pathology image segmentation challenges

Foucart, A., Debeir, O., & Decaestecker, C. (2022) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2022.102155 · Cited by 18

PathNarratives: Data annotation for pathological human-AI collaborative diagnosis

Zhang, H., He, Y., Wu, X., Huang, P., Qin, W., Wang, F., Ye, J., Huang, X., Liao, Y., Chen, H., et al. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2022.1070072 · Cited by 19

Whole slide images in artificial intelligence applications in digital pathology: challenges and pitfalls

Başak, K., Ozyoruk, K. B., & Demir, D. (2023) · Turkish Journal of Pathology · doi:10.5146/tjpath.2023.01601 · Cited by 18

Multi-Omics and Management of Follicular Carcinoma of the Thyroid

Luvhengo, T. E., Bombil, I., Mokhtari, A., Moeng, M. S., Demetriou, D., Sanders, C., & Dlamini, Z. (2023) · Biomedicines · doi:10.3390/biomedicines11041217 · Cited by 21

A novel deep learning-based algorithm combining histopathological features with tissue areas to predict colorectal cancer survival from whole-slide images

Li, Y., Chou, H., Lin, P., Shen, M., & Hsieh, S. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04530-8 · Cited by 18

Lusca: FIJI (ImageJ) based tool for automated morphological analysis of cellular and subcellular structures

Šimunić, I., Jagečić, D., Isaković, J., Radmilović, M. D., & Mitrečić, D. (2024) · Scientific Reports · doi:10.1038/s41598-024-57650-6 · Cited by 18

SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology

Juyal, D., Shingi, S., Javed, S. A., Padigela, H., Shah, C., Sampat, A., Khosla, A., Abel, J., & Taylor-Weiner, A. N. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00776 · Cited by 19

Histopathological image classification through discriminative feature learning and mutual information-based multi-channel joint sparse representation

Li, X., Tang, H., Zhang, D., Liu, T., Mao, L., & Chen, T. (2020) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2020.102799 · Cited by 18

Quantitative analysis of blood cells from microscopic images using convolutional neural network

Tessema, A. W., Mohammed, M. A., Simegn, G. L., & Kwa, T. (2021) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02291-w · Cited by 18

Next-generation protein analysis in the pathology department

Ahmed, M., Broeckx, G., Baggerman, G., Schildermans, K., Pauwels, P., Craenenbroeck, A. H. V., & Dendooven, A. (2019) · Journal of Clinical Pathology · doi:10.1136/jclinpath-2019-205864 · Cited by 18

Segmentation of Prognostic Tissue Structures in Cutaneous Melanoma Using Whole Slide Images

Phillips, A., Teo, I. Y., & Lang, J. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00332 · Cited by 18

Integration of Federated Learning and Blockchain in Healthcare: A TutorialPreprint

Shahsavari, Y., Dambri, O. A., Baseri, Y., Hafid, A. S., & Makrakis, D. (2024) · doi:10.36227/techrxiv.171340632.25905526/v1 · Cited by 20

Sample Specific Generalized Cross Entropy for Robust Histology Image Classification

Kurian, N. C., Meshram, P. S., Patil, A., Patel, S., & Sethi, A. (2021) · 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434169 · Cited by 19

Machine learning based gray-level co-occurrence matrix early warning system enables accurate detection of colorectal cancer pelvic bone metastases on MRI

Jin, J., Zhou, H., Sun, S., Tian, Z., Ren, H., Feng, J., & Jiang, X. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1121594 · Cited by 17

Nuclei probability and centroid map network for nuclei instance segmentation in histology images

Rashid, S. N., & Fraz, M. M. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08503-2 · Cited by 18

Canine Mammary Tumor Histopathological Image Classification via Computer-Aided Pathology: An Available Dataset for Imaging Analysis

Burrai, G. P., Gabrieli, A., Polinas, M., Murgia, C., Becchere, M. P., Demontis, P., & Antuofermo, E. (2023) · Animals · doi:10.3390/ani13091563 · Cited by 21

Multi-Configuration Analysis of DenseNet Architecture for Whole Slide Image Scoring of ER-IHC

Ahmad, W. S. H. M. W., Fauzi, M. F. A., Hasan, M. J., Rehman, Z. U., Lee, J. T. H., Khor, S. Y., Looi, L., Abas, F. S., Adam, A., Chan, E. W. L., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3296848 · Cited by 17

Artificial Intelligence in Predicting Microsatellite Instability and KRAS, BRAF Mutations from Whole-Slide Images in Colorectal Cancer: A Systematic Review

Guitton, T., Allaume, P., Rabilloud, N., Rioux-Leclercq, N. C., Henno, S., Turlin, B., Galibert, M., Lièvre, A., Lespagnol, A., Pécot, T., et al. (2023) · Diagnostics · doi:10.3390/diagnostics14010099 · Cited by 22

Artificial Intelligence for Head and Neck Squamous Cell Carcinoma: From Diagnosis to Treatment

Song, B., Yadav, I. S., Tsai, J., Madabhushi, A., & Kann, B. H. (2025) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk-25-472464 · Cited by 21

A dense multi-path decoder for tissue segmentation in histopathology images

Vu, Q. D., & Kwak, J. T. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.03.007 · Cited by 18

Can Behavioral Research Improve Transplant Decision-Making? A Mock Offer Study on the Role of Kidney Procurement Biopsies

Stewart, D. E., Shepard, B., Rosendale, J. D., McGehee, H., Hall, I. E., Gupta, G., Reddy, K. S., Kasiske, B. L., Andreoni, K. A., & Klassen, D. K. (2020) · Kidney360 · doi:10.34067/kid.0000212019 · Cited by 17

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

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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