A method for normalizing histology slides for quantitative analysis

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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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Unsupervised Color Decomposition of Histologically Stained Tissue Samples

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

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Factors to keep in mind when introducing virtual microscopy

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Spectral Imaging and Pathology: Seeing More

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Breast Cancer Histopathological Image Classification Based on Graph Assisted Global Reasoning

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Persistent Homology in Medical Image Processing: A Literature Review

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Fast TILs—A pipeline for efficient TILs estimation in non-small cell lung cancer

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Stroma and lymphocytes identified by deep learning are independent predictors for survival in pancreatic cancer

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From digitized whole‐slide histology images to biomarker discovery: A protocol for handcrafted feature analysis in brain cancer pathology

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Modeling human-perceived quality for the assessment of digitized histopathology color standardization

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Deep Learning Based Analysis in Oncological Studies: Colorectal Cancer Staging

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Unsupervised Deep Learning for Stain Separation and Artifact Detection in Histopathology Images

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Super Gaussian Priors for Blind Color Deconvolution of Histological Images

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Rhabdomyosarcoma Histology Classification using Ensemble of Deep Learning Networks

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Hybrid Attention Mechanism Guided Convolutional Neural Network for Breast Cancer Histology Images Classification

Zhong, Y., Piao, Y., & Zhang, G. (2021) · International Conference on Computer Science and Network Technology (ICCSNT) · doi:10.1109/iccsnt53786.2021.9615395 · Cited by 4

Using Stain Decomposition for Nucleus Segmentation on Multisource H&E Slide Images

Hsu, H.-C., Tsai, H., Gabrani, M., Chung, P., Foncubierta–Rodríguez, A., & Wu, Y. (2021) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci50451.2021.9659856 · Cited by 4

Breast Cancer Classification from Histological Images using Multi-scale Dense Network

Zhou, Y., Zhang, C., & Gao, S. (2022) · International Conference on Consumer Electronics and Computer Engineering (ICCECE) · doi:10.1109/iccece54139.2022.9712760 · Cited by 4

A Method for Unsupervised Semi-Quantification of Inmunohistochemical Staining with Beta Divergences

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Automated staining analysis in digital cytopathology and applications

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Deep Learning on Lossily Compressed Pathology Images: Adverse Effects for ImageNet Pre-trained Models

Fischer, M., Neher, P., GOETZ, M. A., Xiao, S., Almeida, S. D., Schüffler, P. J., Muckenhuber, A., Braren, R., Kleesiek, J., Nolden, M., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16961-8_8 · Cited by 4

Detection on Cell Cancer Using the Deep Transfer Learning and Histogram Based Image Focus Quality Assessment

Bhuiyan, M. R., & Abdullah, J. (2022) · Sensors · doi:10.3390/s22187007 · Cited by 6

H&E Stain Normalization using U-Net

Lee, C.-C., Kuo, P.-T. P., & Peng, C. (2022) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe55377.2022.00014 · Cited by 4

Computational Pathology for Brain Disorders

Jiménez, G., & Racoceanu, D. (2023) · Neuromethods · doi:10.1007/978-1-0716-3195-9_18 · Cited by 4

A Review on Computer-assisted Techniques to Analyze Histopathological Images of the Breast

Nagdeote, S., & Prabhu, S. (2023) · International Conference on Power, Instrumentation, Energy and Control (PIECON) · doi:10.1109/piecon56912.2023.10085880 · Cited by 4

Classification of Histopathological Images from Breast Cancer Patients Using Deep Learning: A Comparative Analysis

Thalakottor, L. A., Shirwaikar, R. D., Pothamsetti, P. T., & Mathews, L. M. (2023) · Critical Reviews in Biomedical Engineering · doi:10.1615/critrevbiomedeng.2023047793 · Cited by 4

Vision Transformers for Breast Cancer Histology Image Classification

Baroni, G. L., Rasotto, L., Roitero, K., Siraj, A. H., & Mea, V. D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-51026-7_2 · Cited by 4

Improving Breast Tumor Multi-Classification from High-Resolution Histological Images with the Integration of Feature Space Data Augmentation

Brancati, N., & Frucci, M. (2024) · Information · doi:10.3390/info15020098 · Cited by 4

Multi-Artifact Detection and Filtering in Digital Pathology Using Intrinsic Image Properties

Kanwar, V., Woicik, A., Dulken, B., Probert, C. S., & McCaw, Z. R. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635902 · Cited by 4

Assisting the examination of large histopathological slides with adaptive forests

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Segmentation of black ink and melanin in skin histopathological images

Kłeczek, P., Lech, M., Jaworek-Korjakowska, J., Dyduch, G., & Tadeusiewicz, R. (2018) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2292859 · Cited by 4

Accelerated ML-Assisted Tumor Detection in High-Resolution Histopathology Images

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Effects of annotation granularity in deep learning models for histopathological images

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A random polygons model of glandular structures in colon histology images

Sirinukunwattana, K., Snead, D. R. J., & Rajpoot, N. (2015) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7164168 · Cited by 4

Color standardization for the immunohistochemically stained tissue section images

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Multi-scale Voting Classifiers for Breast-Cancer Histology Images

Nalepa, J., Piechaczek, S., Myller, M., & Hrynczenko, K. (2018) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-319-98557-2_49 · Cited by 4

Staining of Histopathology Slides Using Image Style Transfer Algorithm

Ganesh, A., Vasanth, N. R., & George, K. (2018) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2018.8628672 · Cited by 4

FAST GPU-ENABLED COLOR NORMALIZATION FOR DIGITAL PATHOLOGYPreprint

Ramakrishnan, G., Anand, D., & Sethi, A. (2019) · arXiv (Cornell University) · doi:10.13140/rg.2.2.34770.61125 · Cited by 4

Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization (MIDOG) Challenge.Preprint

Wilm, F., Breininger, K., & Aubreville, M. (2021) · arXiv (Cornell University) · Cited by 4

Histopathology-based Protein Multiplex Generation using Deep LearningPreprint

Andani, S., Chen, B., Ficek, J., Heinke, S., Casanova, R., Hild, B. F., Sobottka, B., Bodenmiller, B., Koelzer, V. H., & Rätsch, G. (2024) · medRxiv · doi:10.1101/2024.01.26.24301803 · Cited by 4

ecPath detects ecDNA in tumors from histopathology imagesPreprint

Choudhury, M., Liu, L., Yadav, A., Chapman, O. S., Ahmadi, Z., Younis, R., Sharma, C., Goel, N., Sridhar, S., Kenkre, R., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.13.623494 · Cited by 4

Self organisation of invasive breast cancer driven by the interplay of active and passive nematic dynamicsPreprint

Gottheil, P., Bhattacharyya, S., Lettl, K., Friedrich, P., Roth, K., Rivera-Moreno, S., Merkel, M., Aktas, B., Sauer, I. M., Daneshgar, A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.09.626295 · Cited by 4

Histomolekulare Klassifikation des Urothelkarzinoms der Harnblase

Stoll, A. K., Koll, F. J., Eckstein, M., Reis, H., Flinner, N., Wild, P. J., & Triesch, J. (2024) · Die Pathologie · doi:10.1007/s00292-024-01305-w · Cited by 3

Optimizing immunofluorescence with high-dynamic-range imaging to enhance PD-L1 expression evaluation for 3D pathology assessment from NSCLC tumor tissue

Huang, H., Kuo, C.-W., Hung, Y.-L., Yang, C.-H., Hsieh, Y.-H., Lin, Y.-C., Chang, M. D., Lin, Y.-Y., & Ko, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-65187-x · Cited by 3

Myelin water imaging of in vivo and ex vivo human brains using multi‐echo gradient echo at 3 T and 7 T

Xu, G., Zhao, Z., Zhu, Q., Zhu, K., Zhang, J., & Wu, D. (2024) · Magnetic Resonance in Medicine · doi:10.1002/mrm.30310 · Cited by 3

The tumour histopathology “glossary” for AI developers

Mandal, S., Baker, A., Graham, T. A., & Bräutigam, K. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012708 · Cited by 4

ColorI-DT : An open-source tool for the quantitative evaluation of differences in microscopy color images

Piccinini, F., Tritto, M., Pyun, J., Lee, M., Kwak, B., Ku, B., Normanno, N., & Castellani, G. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.06.019 · Cited by 3

Spatial features of tumor-infiltrating lymphocytes in primary lesions of lung adenocarcinoma predict lymph node metastasis

Zhang, H., Luo, M., Feng, J., Tan, J., Jiang, Y., Frishman, D., & Liu, Y. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06860-1 · Cited by 3

Pathology Foundation Models Are Scanner Sensitive: Benchmark and Mitigation with Contrastive ScanGen Loss

Carloni, G., Brattoli, B., Keum, S., Park, J., Lee, T., Ahn, C. H., & Pereira, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-07845-2_5 · Cited by 3

Diffuse glioma classification with deep learning and explainability: addressing challenges in histopathology image analysis

Jahan, I., Al-Saady, R., Vranić, S., & Chowdhury, M. E. H. (2025) · Soft Computing · doi:10.1007/s00500-025-10932-1 · Cited by 3

The utility of artificial intelligence and deep learning to automate and accelerate follicle counts in human ovarian tissue

Arlova, A., Maher, J. Y., Anvari, H., Badger, T. C., Hanfling, S. N., Balasubramanian, R., Lou, H., Sierra, M. D. L. L., Yun, B. H., Türkbey, B., et al. (2025) · Biology of Reproduction · doi:10.1093/biolre/ioaf250 · Cited by 4

AI-Driven Pathomics for Predicting Chemotherapy Response in Metastatic Colorectal Cancer: A Transfer Learning Approach with Attention-Based Multiple Instance Learning

Cruciani, F., Nicoletti, G., Cafaro, D., Mauri, G., Lazzari, L., Aquilano, M. C., Regge, D., Marsoni, S., & Giannini, V. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11253533 · Cited by 3

Prior knowledge-guided multimodal deep learning system for biomarker exploration and prognosis prediction of urothelial carcinoma

He, Q., Tan, H., Xiao, B., Tan, Y., Peng, X., Peng, C., Yue, X., Jiang, L., Cao, Y., Lv, F. J., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02232-7 · Cited by 3

Interpretable multimodal deep learning improves postoperative risk stratification in intrahepatic cholangiocarcinoma in multicentre cohorts

Wan, M., Ding, Y., Wang, Y., Jia, Y., Wu, S., Qu, W., Xu, Y., Fu, W., Timko, M. P., Wan, L., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02282-x · Cited by 4

Classification accuracy of a hierarchical molecular inference-based deep-learning system for CNS tumour diagnosis: a multi-institutional, retrospective study

Lalchungnunga, H., Dampier, C. H., Singh, O., Hoang, D.-T., Shulman, E. D., Abdullaev, Z., Li, B., Luo, Z., Wu, Z., Pearce, T. M., et al. (2026) · The Lancet Oncology · doi:10.1016/s1470-2045(25)00661-8 · Cited by 3

CMAF-Net: cross-modal attention fusion with information-theoretic regularization for imbalanced breast cancer histopathology

Ativi, W. X., Chen, W., Kwao, L., Ayivi, W., Sam, F., Alqahtani, A., Gu, Y. H., & Al–antari, M. A. (2026) · Scientific Reports · doi:10.1038/s41598-025-32794-1 · Cited by 4

Machine Learning Techniques for Automated Nuclear Atypia Detection in Histopathology Images: A Review

Varghese, J., & Saleema, J. S. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-7402-1_51 · Cited by 3

TrueTH: A user-friendly deep learning approach for robust dopaminergic neuron detection

Chen, J., Meng, Q., Zhang, Y., Liang, Y., Ding, J., Xia, X., & Hu, G. (2024) · Neuroscience Letters · doi:10.1016/j.neulet.2024.137871 · Cited by 3

Deep Learning Histology for Prediction of Lymph Node Metastases and Tumor Regression after Neoadjuvant FLOT Therapy of Gastroesophageal Adenocarcinoma

Jung, J., Pisula, J. I., Beyerlein, X., Lukomski, L., Knipper, K., Hejleh, A. P. A., Fuchs, H. F., Tolkach, Y., Chon, S., Nienhüser, H., et al. (2024) · Cancers · doi:10.3390/cancers16132445 · Cited by 6

Test-Time Stain Adaptation with Diffusion Models for Histopathology Image Classification

Tsai, C.-C., Chen, Y.-C., & Lu, C.-S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72761-0_15 · Cited by 3

High RRM2 Correlates with Mitochondrial and Immune Responses in the Eosinophilic Subtype of Clear Cell Renal Cell Carcinoma

Zhu, X., Al-Danakh, A. Y., Jian, Y., Safi, M., Luo, S., Chen, Q., Wang, S., & Yang, D. (2024) · Journal of Inflammation Research · doi:10.2147/jir.s478993 · Cited by 3

Mi-DETR: For Mitosis Detection From Breast Histopathology Images an Improved DETR

Ardaç, F. B. K., & Erdoğmuş, P. (2024) · IEEE Access · doi:10.1109/access.2024.3492275 · Cited by 6

Deep Multiple Instance Learning Model to Predict Outcome of Pancreatic Cancer Following Surgery

Truntzer, C., Ouahbi, D., Huppé, T., Rageot, D., Ilie, A., Molimard, C., Beltjens, F., Bergeron, A., Vienot, A., Borg, C., et al. (2024) · Biomedicines · doi:10.3390/biomedicines12122754 · Cited by 3

A Good Feature Extractor Is All You Need for Weakly Supervised Pathology Slide Classification

Wölflein, G., Ferber, D., Meneghetti, A. R., Nahhas, O. S. M. E., Truhn, D., Carrero, Z. I., Harrison, D. J., Arandjelović, O., & Kather, J. N. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91721-9_5 · Cited by 4

DEEP LEARNING METHODOLOGIES FOR NUCLEI SEGMENTATION AND MITOSIS DETECTION IN HISTOPATHOLOGICAL IMAGES ANALYSIS

Nemati, N., Samet, R., Hançer, E., Sak, S. D., Kırmızı, A., & Yıldırım, Z. (2025) · Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi · doi:10.17780/ksujes.1613789 · Cited by 3

Multi-modal characterization of metabolic and immune gene clusters in adrenocortical carcinoma treatment

Hao, W., Yao, L., Wang, Y., Wan, J., Zhu, Y., Dai, Z., Sun, X., Fan, B., Wang, Y., Xiang, H., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-01092-4 · Cited by 3

A vocabulary for the identification and delineation of teratoma tissue components in hematoxylin and eosin-stained samples

Bhagavatula, R., McCann, M. T., Fickus, M. C., Castro, C. A., Ozolek, J. A., & Kovačević, J. (2014) · Journal of Pathology Informatics · doi:10.4103/2153-3539.135606 · Cited by 3

Color processing in pathology image analysis system for liver biopsy

Murakami, Y., Abe, T., Yamashita, Y., Yamaguchi, M., Ishikawa, M., Hashiguchi, A., Kiyuna, T., Saito, A., & Sakamoto, M. (2014) · Color and Imaging Conference · doi:10.2352/cic.2014.22.1.art00032 · Cited by 3

An efficient deep neural network for classification in esophageal cancer histopathological images (CNN+LSTM)

Ge, J., Xie, Y., & Wang, D. (2019) · International Conference on Graphics and Image Processing (ICGIP) · doi:10.1117/12.2524240 · Cited by 3

A Hybrid Approach for Automatic Breast Cancer Detection

Bouziane, A., Boumali, S., Berkane, N., & Guendouz, F. S. (2020) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb50910.2020.9280280 · Cited by 3

Dual-Stage Domain Adaptive Mitosis Detection for Histopathology Images

Dodballapur, V., Song, Y., Huang, H., Chen, M., Chrzanowski, W., & Cai, W. (2020) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta51227.2020.9363411 · Cited by 3

Predicting Clear Cell Renal Cell Carcinoma Survival Using Kurtosis of Cytoplasm in the Hematoxylin Channel from Histology Slides

Wang, J., Chen, J., Jiang, L., Wu, Q., & Wang, D. (2022) · Journal of Oncology · doi:10.1155/2022/7693993 · Cited by 3

Explicabilité et interprétabilité en Intelligence Artificielle : vers une IA responsable - Instanciation dans le domaine de la santé

Racoceanu, D., Ounissi, M., & KERGOSIEN, Y. L. (2022) · Technologies logicielles Architectures des systèmes · doi:10.51257/a-v1-h5030 · Cited by 3

Application of AI in Histopathological Image Analysis

Štifanić, J., Štifanić, D., Zulijani, A., & Car, Z. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-29717-5_9 · Cited by 3

Malignant breast lesions detection in histopathological images based on the combination of bioinspired texture descriptors and deep features

Luz, D. D. S., Bisneto, T. R. V., Lima, T. J. B., Mathew, M. J., & Araújo, F. H. D. (2023) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2023.2198037 · Cited by 3

Proliferation score prediction using novel SMHC feature using adaptive XGBoost model

Krithiga, R., & Geetha, P. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15987-6 · Cited by 4

Deep Bayesian Blind Color Deconvolution of Histological Images

Yang, S., Pérez-Bueno, F., Castro-Macías, F. M., Molina, R., & Katsaggelos, A. K. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222193 · Cited by 3

Feature Selection and Comparative Analysis of Breast Cancer Prediction Using Clinical Data and Histopathological Whole Slide Images

Mohammed, S. A., Abeysinghe, S. D., & Ralescu, A. (2023) · Advances in Artificial Intelligence and Machine Learning · doi:10.54364/aaiml.2023.1187 · Cited by 3

Dense Prediction of Cell Centroids Using Tissue Context and Cell Refinement

Millward, J., He, Z., & Nibali, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-55088-1_13 · Cited by 3

Visual Intratumor Heterogeneity and Breast Tumor Progression

Li, Y., Alsten, S. C. V., Lee, D. N., Kim, T., Calhoun, B. C., Perou, C. M., Wobker, S. E., Marron, J. S., Hoadley, K. A., & Troester, M. A. (2024) · Cancers · doi:10.3390/cancers16132294 · Cited by 5

Classification of H&E Stained Liver Histopathology Images Using Ensemble Learning Techniques for Detection of the Level of Malignancy of Hepatocellular Carcinoma (HCC)

Rukmangad, A., Deshpande, A., Jamthikar, A. D., Gupta, D., Bhurane, A. A., & Meshram, N. B. (2024) · Learning and analytics in intelligent systems · doi:10.1007/978-3-031-62316-5_3 · Cited by 3

Data efficient contrastive learning in histopathology using active sampling

Reasat, T., Sushmit, A. S., & Smith, D. S. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100577 · Cited by 5

A novel computational pathology approach for identifying gene signatures prognostic of disease-free survival for papillary thyroid carcinomas

Monabbati, S., Khalighi, S., Fu, P., Shi, Q., Asa, S. L., & Madabhushi, A. (2024) · European Journal of Cancer · doi:10.1016/j.ejca.2024.114326 · Cited by 3

Cluster Triplet Loss for Unsupervised Domain Adaptation on Histology Images

Wood, R., Domingo, E., Koelzer, V. H., Maughan, T., & Rittscher, J. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00519 · Cited by 4

URCDM: Ultra-Resolution Image Synthesis in Histopathology

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Causal debiasing for unknown bias in histopathology—A colon cancer use case

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