A method for normalizing histology slides for quantitative analysis

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Improving Melanoma Classification by Integrating Genetic and Morphologic Features

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Distance-Weighted Discrimination

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Comparison of Quantification of Histochemical Staining By Hue-Saturation-Intensity (HSI) Transformation and Color-Deconvolution

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A Framework for the Automated Analysis of Subcellular Patterns in Human Protein Atlas Images

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Spectral Imaging and Pathology: Seeing More

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Methodological aspects on immunohistochemistry in dermatology with special reference to neuronal markers

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Evaluation of Fuzzy Measures Using Dempster–Shafer Belief Structure: A Classifier Fusion Framework

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A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations

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Dense Dilated Multi-Scale Supervised Attention-Guided Network for histopathology image segmentation

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Grading of lung adenocarcinomas with simultaneous segmentation by artificial intelligence (GLASS-AI)

Lockhart, J. H., Ackerman, H. D., Lee, K., Abdalah, M. A., Davis, A. J., Hackel, N., Boyle, T. A., Saller, J. J., Keske, A., Hänggi, K., et al. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00419-3 · Cited by 12

A Fast and Scalable Pipeline for Stain Normalization of Whole-Slide Images in Histopathology

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WBCaps: A Capsule Architecture-based Classification Model Designed for White Blood Cells Identification

Liu, Y., Fu, Y., & Chen, P. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856700 · Cited by 12

Artificial intelligence modelling in differentiating core biopsies of fibroadenoma from phyllodes tumor

Cheng, C. L., Nasir, N. D. M., Ng, G. J. Z., Chua, K. W. J., Li, Y., Rodrigues, J. S., Thike, A. A., Heng, S. Y., Koh, V. C. Y., Lim, J. X., et al. (2021) · Laboratory Investigation · doi:10.1038/s41374-021-00689-0 · Cited by 12

Outcome and biomarker supervised deep learning for survival prediction in two multicenter breast cancer series

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Automatic Detection of Bacilli Bacteria from Ziehl-Neelsen Sputum Smear Images

Shwetha, V., Prasad, K., Mukhopadhyay, C., Banerjee, B., & Chakrabarti, A. (2021) · International Conference on Communication, Computing and Industry 4.0 (C2I4) · doi:10.1109/c2i454156.2021.9689283 · Cited by 12

Virtual liver needle biopsy from reconstructed three-dimensional histopathological images: Quantification of sampling error

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A multi-path approach to histology volume reconstruction

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A panoptic segmentation approach for tumor-infiltrating lymphocyte assessment: development of the MuTILs model and PanopTILs datasetPreprint

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HistomicsTK: A Python toolkit for pathology image analysis algorithms

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Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images

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FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation

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A mixture of experts (MoE) model to improve AI-based computational pathology prediction performance under variable levels of image blur

Xiang, Y., Liu, B., & Rantalainen, M. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01974-w · Cited by 10

Path2Omics Enhances Transcriptomic and Methylation Prediction Accuracy from Tumor Histopathology

Hoang, D.-T., Shulman, E. D., Dhruba, S. R., Nair, N. U., Barman, R. K., Cantore, T., Biswas, S., Lalchungnunga, H., Singh, O., Chung, Y., et al. (2025) · Cancer Research · doi:10.1158/0008-5472.can-25-4350 · Cited by 14

Attention-based multimodal fusion transformer for predicting the efficacy of neoadjuvant therapy in breast cancer: a cross-institutional retrospective study

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Categorization of Breast Carcinoma Histopathology Images by Utilizing Region-Based Convolutional Neural Networks

Altuntaş, T. S., Toyran, T., & Arıca, S. (2023) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-023-08387-3 · Cited by 11

AI-Based Risk Score from Tumour-Infiltrating Lymphocyte Predicts Locoregional-Free Survival in Nasopharyngeal Carcinoma

Wibawa, M. S., Zhou, J., Wang, R., Huang, Y., Zhan, Z., Chen, X., Lv, X., Young, L. S., & Rajpoot, N. (2023) · Cancers · doi:10.3390/cancers15245789 · Cited by 12

DeepTree: Pathological Image Classification Through Imitating Tree-Like Strategies of Pathologists

Li, J., Cheng, J., Meng, L., Yan, H., He, Y., Shi, H., Guan, T., & Han, A. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3341846 · Cited by 11

Artificial Intelligence Models for the Detection of Microsatellite Instability from Whole-Slide Imaging of Colorectal Cancer

Faa, G., Coghe, F., Pretta, A., Castagnola, M., Eyken, P. V., Saba, L., Scartozzi, M., & Fraschini, M. (2024) · Diagnostics · doi:10.3390/diagnostics14151605 · Cited by 11

Restoring H&E stain in faded slides via phase-to-color virtual staining in near-infrared

Chae, H., Kim, J. H., Jeon, J., Lee, K., Lee, K. C., Choi, J. U., Kang, S., Choi, S., Bang, G., Lee, J.-H., et al. (2024) · APL Photonics · doi:10.1063/5.0226277 · Cited by 11

Deeply supervised two stage generative adversarial network for stain normalization

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From Silos to Synthesis: A comprehensive review of domain adaptation strategies for multi-source data integration in healthcare

Tuly, S. R., Ranjbari, S., Murat, E. A., & Arslanturk, S. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110108 · Cited by 13

Multimodal AI model for preoperative prediction of axillary lymph node metastasis in breast cancer using whole slide images

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NCT-CRC-HE: Not All Histopathological Datasets are Equally Useful

Ignatov, A., & Malivenko, G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91721-9_19 · Cited by 13

Classification of non-Hodgkin lymphomas based on sample entropy signatures

Rozendo, G. B., do Nascimento, M. Z., Roberto, G. F., de Faria, P. R., Silva, A. B., Tosta, T. A. A., & Neves, L. A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117238 · Cited by 11

Robust Image Population Based Stain Color Normalization: How Many Reference Slides Are Enough?

Agraz, J. L., Grenko, C. M., Chen, A. A., Viaene, A. N., Nasrallah, M. D., Pati, S., Kurç, T., Saltz, J. H., Feldman, M. D., Akbari, H., et al. (2022) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2023.3234443 · Cited by 11

An effective approach for the nuclei segmentation from breast histopathological images using star-convex polygon

Nelson, A. D., & Krishna, S. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.01.156 · Cited by 13

Color Normalization Approach to Adjust Nuclei Segmentation in Images of Hematoxylin and Eosin Stained Tissue

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Cross-Stained Segmentation from Renal Biopsy Images Using Multi-Level Adversarial Learning

Mei, K., Zhu, C., Jiang, L., Liu, J., & Qiao, Y. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054505 · Cited by 11

Accurate segmentation of prostate cancer histomorphometric features using a weakly supervised convolutional neural network

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Improved Domain Generalization for Cell Detection in Histopathology Images via Test-Time Stain Augmentation

Xu, C., Wen, Z., Liu, Z., & Ye, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16434-7_15 · Cited by 11

Neural Stain Normalization and Unsupervised Classification of Cell Nuclei in Histopathological Breast Cancer ImagesPreprint

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Deep Object Detection based Mitosis Analysis in Breast Cancer Histopathological ImagesPreprint

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Deep feature batch correction using ComBat for machine learning applications in computational pathology

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Characterization of color normalization methods in digital pathology whole slide imaging

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A Laplacian Pyramid Based Generative H&E Stain Augmentation Network

Li, F., Hu, Z., Chen, W., & Kak, A. C. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3317239 · Cited by 10

3D multiplexed tissue imaging reconstruction and optimized region of interest (ROI) selection through deep learning model of channels embedding

Burlingame, E. A., Ternes, L., Lin, J., Chen, Y.-A., Kim, E. N., Gray, J. W., Santagata, S., Sorger, P. K., & Chang, Y. H. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1275402 · Cited by 10

An interpretable deep learning model for identifying the morphological characteristics of dMMR/MSI-H gastric cancer

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Deep Neural Networks for HER2 Grading of Whole Slide Images with Subclasses Levels

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Nextflow pipeline for Visium and H&E data from patient-derived xenograft samples

Domanskyi, S., Srivastava, A., Kaster, J., Li, H., Herlyn, M., Rubinstein, J. C., & Chuang, J. H. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100759 · Cited by 10

A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer

Patkar, S., Harmon, S. A., Sesterhenn, I. A., Lis, R. T., Merino, M. J., Young, D., Brown, G. T., Greenfield, K. M., McGeeney, J. D., Elsamanoudi, S. I., et al. (2024) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2024.100381 · Cited by 10

Improving quality control of whole slide images by explicit artifact augmentation

Jurgas, A., Wodziński, M., D’Amato, M., van der Laak, J. A. W. M., Atzori, M., & Müller, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-68667-2 · Cited by 10

ATEC23 Challenge: Automated prediction of treatment effectiveness in ovarian cancer using histopathological images

Wang, C., Firdi, N. P., Chu, T.-C., Faiz, M. F. I., Iqbal, M. Z., Li, Y., Yang, B., Mallya, M., Bashashati, A., Li, F., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103342 · Cited by 10

DenseNet-FPA: Integrating DenseNet and Flower Pollination Algorithm for Breast Cancer Histopathology Image Classification

Wakili, M. A., Shehu, H. A., Abdollahi, M., Ya’u, B. I., Sharif, M. H., & Kusetoğulları, H. (2025) · IEEE Access · doi:10.1109/access.2025.3599319 · Cited by 10

A robust nonlinear tissue-component discrimination method for computational pathology

Sarnecki, J. S., Burns, K. H., Wood, L. D., Waters, K. M., Hruban, R. H., Wirtz, D., & Wu, P.-H. (2016) · Laboratory Investigation · doi:10.1038/labinvest.2015.162 · Cited by 10

Artificial intelligence for pathology

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Self-supervision Based Dual-Transformation Learning for Stain Normalization, Classification andSegmentation

Gehlot, S., & Gupta, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87589-3_49 · Cited by 10

Spectral decoupling for training transferable neural networks in medical imaging

Pohjonen, J., Stürenberg, C., Rannikko, A. S., Mirtti, T. K., & Pitkänen, E. (2022) · iScience · doi:10.1016/j.isci.2022.103767 · Cited by 10

Staining condition visualization in digital histopathological whole-slide images

Jiao, Y., Li, J., & Fei, S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12559-y · Cited by 10

Automatic Placental Distal Villous Hypoplasia Scoring using a Deep Convolutional Neural Network Regression Model

Khodaee, A., Grynspan, D., Bainbridge, S. A., Ukwatta, E., & Chan, A. D. C. (2022) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc48687.2022.9806589 · Cited by 10

How to learn with intentional mistakes: NoisyEnsembles to overcome poor tissue quality for deep learning in computational pathology

Mayer, R. S., Gretser, S., Heckmann, L., Ziegler, P. K., Walter, B., Reis, H., Bankov, K., Becker, S., Triesch, J., Wild, P. J., et al. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.959068 · Cited by 10

Prediction of microsatellite instability of colorectal cancer using multi-scale pathological images based on deep learning

Cao, R., Gu, Q., Tan, D., Wei, P.-J., & Zheng, C.-H. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995576 · Cited by 10

Predicting melanoma survival and metastasis with interpretable histopathological features and machine learning models

Couetil, J. L., Liu, Z., Huang, K., Zhang, J., & Alomari, A. K. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2022.1029227 · Cited by 10

Cross-Domain Nuclei Detection in Histopathology Images Using Graph-Based Nuclei Feature Alignment

Wang, Z., Fan, K., Zhu, X., Liu, H., Meng, G., Wang, M., & Li, A. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3280958 · Cited by 10

Unsupervised color normalisation for H and E stained histopathology image analysis

Celis, R. L. R., & Romero, E. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2211536 · Cited by 10

Towards Interactive Breast Tumor Classification Using Transfer Learning

Weiss, N., Höfener, H., & Homeyer, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93000-8_82 · Cited by 11

Deep Convolutional Neural Networks for Breast Cancer Detection

Roy, A. (2019) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon47517.2019.8993023 · Cited by 10

Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential

Tschuchnig, M., Oostingh, G. J., & Gadermayr, M. (2020) · Patterns · doi:10.1016/j.patter.2020.100144 · Cited by 10

Deep Learning Methods in Medical Imaging for the Recognition of Breast Cancer

Chorianopoulos, A. M., Daramouskas, I., Perikos, I., Grivokostopoulou, F., & Hatzilygeroudis, I. (2020) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa50023.2020.9284373 · Cited by 10

Multi-class Tissue Classification in Colorectal Cancer with Handcrafted and Deep Features

Altini, N., Marvulli, T. M., Caputo, M., Mattioli, E., Prencipe, B., Cascarano, G. D., Brunetti, A., Tommasi, S., Bevilacqua, V., Summa, S. D., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-84522-3_42 · Cited by 10

HistoClean: Open-source software for histological image pre-processing and augmentation to improve development of robust convolutional neural networks

McCombe, K. D., Craig, S. G., Pulsawatdi, A. V., Quezada-Marín, J. I., Hagan, M., Rajendran, S., Humphries, M. P., Bingham, V., Salto‐Tellez, M., Gault, R., et al. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.08.033 · Cited by 10

Tau protein discrete aggregates in Alzheimer's disease: neuritic plaques and tangles detection and segmentation using computational histopathology

Manouskova, K., Abadie, V., Ounissi, M., Jiménez, G., Stimmer, L., Delatour, B., Durrleman, S., & Racoceanu, D. (2022) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2613154 · Cited by 10

Artificial Intelligence-Based Tissue Phenotyping in Colorectal Cancer Histopathology Using Visual and Semantic Features Aggregation

Mahmood, T., Kim, S. G., Koo, J. H., & Park, K. R. (2022) · Mathematics · doi:10.3390/math10111909 · Cited by 10

Classifying Cervical Histopathological Whole Slide Images via Deep Multi-Instance Transfer Learning

Jiang, P., Liu, J., Wang, L., Feng, J. Q., Cao, D., & Pang, B. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995014 · Cited by 11

A Grading Strategy for Nuclear Pleomorphism in Histopathological Breast Cancer Images Using a Bag of Features (BOF)

Moncayo, R. A., Romo‐Bucheli, D., & Romero, E. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-25751-8_10 · Cited by 10

Pre-processing Techniques for Colour Digital Pathology Image Analysis

Saafin, W., & Schaefer, G. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-60964-5_48 · Cited by 10

Fully automated registration of vibrational microspectroscopic images in histologically stained tissue sections

Yang, C., Niedieker, D., Großerueschkamp, F., Horn, M., Tannapfel, A., Kallenbach‐Thieltges, A., Gerwert, K. B., & Mosig, A. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0804-9 · Cited by 10

An open source pipeline for quantitative immunohistochemistry image analysis of inflammatory skin disease using artificial intelligence

Ding, Y., Dhawan, G., Jones, C., Ness, T., Nichols, E., Krasnogor, N., & Reynolds, N. J. (2023) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.18726 · Cited by 9

Direct prediction of Homologous Recombination Deficiency from routine histology in ten different tumor types with attention-based Multiple Instance Learning: a development and validation study

Lavinia Loeffler, C. M., El Nahhas, O. S. M., Muti, H. S., Seibel, T., Cifci, D., Treeck, M. V., Gustav, M., Carrero, Z. I., Gaisa, N. T., Lehmann, K.-V., et al. (2023) · doi:10.1101/2023.03.08.23286975 · Cited by 12

Comparison between renal pelvic and ureteral tumors in muscle‐invasive upper tract urothelial carcinoma

Chen, Q., Shi, J., Liang Cheng, C., Huang, S., Kuai, Y., Shujing, W., Liu, W., Zhu, X., Hongyu, W., & Yang, D. (2022) · Cancer Science · doi:10.1111/cas.15634 · Cited by 9

A comprehensive review of tubule formation in histopathology images: advancement in tubule and tumor detection techniques

Siet, J. J. W., Tan, X. J., Cheor, W. L., Rahman, K. S. A., Cheng, E. M., Muhamad, W. Z. A. W., & Yip, S. Y. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10887-z · Cited by 9

Re-identification from histopathology images

Ganz, J., Ammeling, J., Jabari, S., Breininger, K., & Aubreville, M. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103335 · Cited by 14

Deep learning‐based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images

Park, J. H., Lim, J. H., Kim, S., Kim, C., Choi, J., Lim, J. H., Kim, L., Chang, J. W., Park, D., Lee, M.-W., et al. (2024) · The Journal of Pathology Clinical Research · doi:10.1002/2056-4538.70004 · Cited by 10

Quantitative Modeling to Characterize Maternal Inflammatory Response of Histologic Chorioamnionitis in Placental Membranes

Chou, T., Senkow, K. J., Nguyen, M. B., Patel, P. V., Sandepudi, K., Cooper, L., & Goldstein, J. A. (2024) · American Journal of Reproductive Immunology · doi:10.1111/aji.13944 · Cited by 9

Aggressiveness classification of clear cell renal cell carcinoma using registration‐independent radiology‐pathology correlation learning

Bhattacharya, I., Stacke, K., Chan, E., Lee, J. H., Tse, J. R., Liang, T., Brooks, J. D., Sonn, G. A., & Rusu, M. (2024) · Medical Physics · doi:10.1002/mp.17476 · Cited by 9

Prognostic and predictive value of pathohistological features in gastric cancer and identification of SLITRK4 as a potential biomarker for gastric cancer

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MorphoITH: a framework for deconvolving intra-tumor heterogeneity using tissue morphology

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