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

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Out-of-distribution generalization for segmentation of lymph node metastasis in breast cancer

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Abnormality-aware multimodal learning for WSI classification

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F2FLDM: Latent Diffusion Models with Histopathology Pre-Trained Embeddings for Unpaired Frozen Section to FFPE Translation

Ho, M. M., Dubey, S., Chong, Y., Knudsen, B. S., & Taşdizen, T. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00430 · Cited by 9

Classification of H&E Stained Breast Cancer Histopathology Images Based on Convolutional Neural Network

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A non-alternating graph hashing algorithm for large-scale image search

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Histological Image Segmentation and Classification Using Entropy-Based Convolutional Module

Kim, H.-R., Kim, K., Lim, K.-T., & Choi, D.-H. (2021) · IEEE Access · doi:10.1109/access.2021.3091578 · Cited by 6

Dysplasia Grading of Colorectal Polyps Through Convolutional Neural Network Analysis of Whole Slide Images

Perlo, D., Tartaglione, E., Bertero, L., Cassoni, P., & Grangetto, M. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-3880-0_34 · Cited by 8

Stain Style Transfer for Histological Images Using S3CGAN

Lee, J.-S., & Ma, Y.-X. (2022) · Sensors · doi:10.3390/s22031044 · Cited by 6

Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis

Wilm, F., Marzahl, C., Breininger, K., & Aubreville, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-97281-3 · Cited by 6

Performance Analysis of Color Normalization Methods in Histopathology Images

Yee, W. C., Jian, T. X., Rahman, K. S. A., Hoe, T. L., Min, L. J., Hang, Q. Y., & Ling, T. C. (2022) · IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) · doi:10.1109/i2cacis54679.2022.9815475 · Cited by 6

An Explainable Coarse-to-Fine Survival Analysis Method on Multi-Center Whole Slide Images

Wang, H., Jiang, D., Zhang, H., Wang, Y., Yang, L., Kerr, D. J., & Zhang, Y. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3285855 · Cited by 6

Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis

Azher, Z., Fatemi, M., Lu, Y., Srinivasan, G., Diallo, A., Christensen, B. C., Salas, L. A., Kolling, F., Perreard, L., Palisoul, S. M., et al. (2023) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811286421_0036 · Cited by 5

Improving prediction of treatment response and prognosis in colorectal cancer with AI-based medical image analysis

Liu, X., Zhang, S., Shao, L., Sun, C., Li, B., Wei, W., Ying, Z., Liu, Z., & Tian, J. (2024) · The Innovation Medicine · doi:10.59717/j.xinn-med.2024.100069 · Cited by 8

Blind Color Deconvolution of Histopathological Images Using a Variational Bayesian Approach

Hidalgo-Gavira, N., Javier Mateos, A., Miguel Vega, B., Molina, R., & Katsaggelos, A. K. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451314 · Cited by 6

Certainty Pooling for Multiple Instance Learning

Gildenblat, J., Ben-Shaul, I., Lapp, Z., & Klaiman, E. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68763-2_11 · Cited by 6

Design of an Efficient Deep Neural Network for Multi-level Classification of Breast Cancer Histology Images

Laxmisagar, H. S., & Hanumantharaju, M. C. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-5566-4_40 · Cited by 6

Computational pathology reveals unique spatial patterns of immune response in H&E images from COVID-19 autopsies: preliminary findings

Corredor, G., Toro, P., Bera, K., Fu, P., Barton, L. M., Gilmore, H. L., Mukhopadhyay, S., Madabhushi, A., Rasmussen, D., Viswanathan, V. S., et al. (2021) · Journal of medical imaging · doi:10.1117/1.jmi.8.s1.017501 · Cited by 6

Primary Tumor and Inter-Organ Augmentations for Supervised Lymph Node Colon Adenocarcinoma Metastasis Detection

Tsirikoglou, A., Stacke, K., Eilertsen, G., & Unger, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87240-3_60 · Cited by 6

A Fast Texture-to-Stain Adversarial Stain Normalization Network for Histopathological Images

Jia, Q., Guo, J. M., Du, F., Yang, P., & Yang, Y. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995102 · Cited by 6

Color Normalization-Based Nuclei Detection in Images of Hematoxylin and Eosin-Stained Multi Organ Tissues

Piórkowski, A. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-31254-1_8 · Cited by 6

stainlib: a python library for augmentation and normalization of histopathology H&E imagesPreprint

Otálora, S., Marini, N., Podareanu, D., Hekster, R., Tellez, D., van der Laak, J. A. W. M., Müller, H., & Atzori, M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.17.492245 · Cited by 8

Classification of Colorectal Polyps from Histopathological Images using Ensemble of ConvNeXt VariantsPreprint

Yengec-Tasdemir, S. B., Akay, E., Doğan, S., & Yılmaz, B. (2022) · Research Square · doi:10.21203/rs.3.rs-1791422/v1 · Cited by 7

A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis

White, B. S., Woo, X. Y., Koc, S., Sheridan, T., Neuhauser, S. B., Wang, S., Evrard, Y. A., Chen, L., pour, A. F., Landua, J. D., et al. (2024) · Cancer Research · doi:10.1158/0008-5472.can-23-1349 · Cited by 5

Deep learning-based histological predictions of chromosomal instability in colorectal cancer

Hyeon, D.-W. (2024) · American Journal of Cancer Research · doi:10.62347/jynd6488 · Cited by 6

A digital pathology model for predicting radioiodine-avid metastases on initial post-therapeutic 131I scan in patients with papillary thyroid cancer

Xue, Y., Zheng, M., Wu, X., Li, B., Ding, X., Liu, S., Liu, S., Liu, Q., & Gao, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-78459-3 · Cited by 5

SAStainDiff: Self-supervised stain normalization by stain augmentation using denoising diffusion probabilistic models

Yang, H., Lyu, M., Yan, S., Zhong, T., Li, J., Xu, T., Xie, H., & Liu, S. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107861 · Cited by 7

Integrating multimodal data to predict the progression of hormone-sensitive prostate cancer

Lu, X. J. D., Pan, C., Yao, L., Wan, J., Xu, X., Wang, W., Wang, X., Liu, X., Jin, Z., Wang, H., et al. (2025) · Clinical Proteomics · doi:10.1186/s12014-025-09543-7 · Cited by 6

Multiparametric cellular and spatial organization in cancer tissue lesions with a streamlined pipeline

Buckup, M., Figueiredo, I., Ioannou, G., Ozbey, S., Cabal, R., Tabachnikova, A., Troncoso, L., Bérichel, J. L., Zhao, Z., Ward, S. C., et al. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01475-9 · Cited by 5

Deep-learning based morphological segmentation of canine diffuse large B-cell lymphoma

Ancheta, K., Psifidi, A., Yale, A. D., Calvez, S. L., & Williams, J. M. (2025) · Frontiers in Veterinary Science · doi:10.3389/fvets.2025.1656976 · Cited by 5

H&E-based MSI/MMR testing with AI in colorectal cancer: a multi-centred blinded evaluation

Bass, C., Ntelemis, F., Schmidt, J., Wolf, S., Geraldes, A., Mehrotra, D. G., Singhal, S., Kumar, N., Marcia, A., Bennett, N., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02218-5 · Cited by 6

Pathomics Signature for Prognosis and CA19‐9 Interception in Pancreatic Ductal Adenocarcinoma: A Real‐Life, Multi‐Center Study

Chen, Q., Xu, Z., Zou, Y., Jiang, Z., Li, Y., He, T., Yin, H., Li, J., An, Y., Han, J., et al. (2026) · Advanced Science · doi:10.1002/advs.202515952 · Cited by 5

Difficulties and Recommendations for AI-Based Prediction of Prostate Cancer Aggressiveness in Digital Pathology

Brehler, M., Walhagen, P., Busch, C., Bonn, S., & Bengtsson, E. W. (2023) · Medical Research Archives · doi:10.18103/mra.v11i11.4586 · Cited by 5

WhARIO: whole-slide-image-based survival analysis for patients treated with immunotherapy

Tourniaire, P., Ilié, M., Mazières, J., Vigier, A., Ghiringhelli, F., Piton, N., Sabourin, J., Bibeau, F., Hofman, P. M., Ayache, N., et al. (2024) · Journal of medical imaging · doi:10.1117/1.jmi.11.3.037502 · Cited by 6

A Quantitative Measurement Method for Nuclear-Pleomorphism Scoring in Breast Cancer

Teoh, C. L., Tan, X. J., Rahman, K. S. A., Bakrin, I. H., Goh, K. M., Siet, J. J. W., & Muhamad, W. Z. A. W. (2024) · Diagnostics · doi:10.3390/diagnostics14182045 · Cited by 5

Learning from Partial Label Proportions for Whole Slide Image Segmentation

Matsuo, S., Suehiro, D., Uchida, S., Ito, H., Terada, K., Yoshizawa, A., & Bise, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72120-5_35 · Cited by 5

Automatic Watershed Segmentation of Cancerous Lesions in Unsupervised Breast Histology Images

Majanga, V., & Mnkandla, E. (2024) · Applied Sciences · doi:10.3390/app142210394 · Cited by 5

HEMIT: H&E to Multiplex-Immunohistochemistry Image Translation with Dual-Branch Pix2pix Generator

Bian, C., Phillips, E. H., Cootes, T. F., & Fergie, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-84525-3_16 · Cited by 5

Unsupervised learning for labeling global glomerulosclerosis

Weishaupt, H., Besusparis, J., Weis, C., Porubský, Š., Laurinavičius, A., & Leh, S. M. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110719 · Cited by 4

Interpretable machine learning-based detection of coeliac disease

Jaeckle, F., Bryant, R., Denholm, J., Diaz, J. R., Schreiber, B., Shenoy, V., Ekundayomi, D., Evans, S. C., Arends, M. J., & Soilleux, E. J. (2025) · BMJ Digital Health & AI · doi:10.1136/bmjdhai-2025-000023 · Cited by 6

Neural Network Classification of Blood Vessels and Tubules Based on Haralick Features Evaluated in Histological Images of Kidney Biopsy

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Cytology image analysis

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Automated Detection of Portal Fields and Central Veins in Whole-Slide Images of Liver Tissue

Budelmann, D., Laue, H., Weiss, N., Dahmen, U., D’Alessandro, L. A., Biermayer, I., Klingmüller, U., Ghallab, A., Hassan, R. A. R. A., Begher‐Tibbe, B., et al. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100001 · Cited by 5

Federated Learning for Colorectal Cancer Prediction

Maurya, Y., Chandrahasan, P., & Poornalatha, G. (2022) · IEEE Global Conference for Advancement in Technology (GCAT) · doi:10.1109/gcat55367.2022.9972224 · Cited by 5

Improving automated thyroid cancer classification of frozen sections by the aid of virtual image translation and stain normalization

Gadermayr, M., Tschuchnig, M., Stangassinger, L. M., Kreutzer, C., Couillard‐Després, S., Oostingh, G. J., & Hittmair, A. (2023) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2023.100092 · Cited by 5

Prediction of Microsatellite Instability From Gastric Histological Images Based on Residual Attention Networks With Non-Local Modules

Yu, S., Huang, S., Wang, W., Chang, Y. P., Chen, K., & Chen, T. (2023) · IEEE Access · doi:10.1109/access.2023.3286309 · Cited by 5

MS-SST: Single Image Reconstruction-Based Stain-Style Transfer for Multi-Domain Hematoxylin & Eosin Stained Pathology Images

Kweon, J., Kim, M., Yun, G., Kwon, S., & Yoo, J. (2023) · IEEE Access · doi:10.1109/access.2023.3274877 · Cited by 5

Deep learning-derived spatial organization features on histology images predicts prognosis in colorectal liver metastasis patients after hepatectomy

Lin, Q., Liang, J., Li, Z., Xi, S., Lai, Y., Gao, F., Zhang, X., Wang, D., Hu, M.-T., Cao, Y., et al. (2023) · iScience · doi:10.1016/j.isci.2023.107702 · Cited by 6

A joint Multi-decoder Dual-attention U-Net framework for tumor segmentation in Whole Slide Images

Abdel-Nabi, H., Ali, M. Z., & Awajan, A. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101835 · Cited by 5

Deep learning system for true- and pseudo-invasion in colorectal polyps

Yang, J., Chen, L., Liu, E. V., Wang, B., Driman, D. K., Zhang, Q., & Ling, C. X. (2024) · Scientific Reports · doi:10.1038/s41598-023-50681-5 · Cited by 5

Surrogate Biomarker Prediction from Whole-Slide Images for Evaluating Overall Survival in Lung Adenocarcinoma

Murchan, P., Baird, A., Broin, P. Ó., Sheils, O. M., & Finn, S. P. (2024) · Diagnostics · doi:10.3390/diagnostics14050462 · Cited by 5

Real-time and accurate deep learning-based multi-organ nucleus segmentation in histology images

Ahmed, N. (2024) · Journal of Real-Time Image Processing · doi:10.1007/s11554-024-01420-0 · Cited by 5

A novel approach for color-balanced reference image selection for breast histology image normalization

Jawad, M. A., & Khursheed, F. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106299 · Cited by 5

A-ReSEUnet: Achieve no-label binary segmentation of nuclei in histology images

Chang, J.-H., Wu, P.-H., Wang, T., & Chung, P. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112217 · Cited by 5

MM-Survnet: Deep Learning-Based Survival Risk Stratification in Breast Cancer Through Multimodal Data Fusion

Mondol, R. K., Millar, E. K. A., Sowmya, A., & Meijering, E. H. W. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635810 · Cited by 5

Leveraging deep learning for identification and segmentation of “CAF-1/p60-positive” nuclei in oral squamous cell carcinoma tissue samples

Varricchio, S., Ilardi, G., Russo, D., Crescenzo, R. M. D., Crispino, A., Staibano, S., & Merolla, F. (2024) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2024.100407 · Cited by 5

Image analysis-based identification of high risk ER-positive, HER2-negative breast cancers

Lee, D. N., Li, Y., Olsson, L. T., Hamilton, A. M., Calhoun, B. C., Hoadley, K. A., Marron, J. S., & Troester, M. A. (2024) · Breast Cancer Research · doi:10.1186/s13058-024-01915-5 · Cited by 5

EMViT ‐ BCC : Enhanced Mobile Vision Transformer for Breast Cancer Classification

Potsangbam, J., & Devi, S. S. (2025) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70053 · Cited by 5

Blind stain decomposition for histo-pathology images using circular nature of chroma components

Li, X., & Plataniotis, K. N. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178095 · Cited by 5

The Accuracy of H&E Stain Unmixing Techniques When Estimating Relative Stain Concentrations

Kłeczek, P., Mól, S., & Jaworek-Korjakowska, J. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-66905-2_7 · Cited by 5

Inferring a third spatial dimension from 2D histological images

Lafarge, M. W., Pluim, J. P. W., Eppenhof, K. A. J., Moeskops, P., & Veta, M. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363644 · Cited by 5

Disentangled Representation Learning Based Multidomain Stain Normalization For Histological Images

Xiang, Y., Chen, J., Liu, Q., & Liang, Y. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9190757 · Cited by 5

Segmentation of Oral Epithelial Dysplasias Employing Mask R-CNN and Color Normalization

Silva, A. B., dos Santos, D. F. D., Tosta, T. A. A., Martins, A. S., Neves, L. A., Travençolo, B. A. N., de Faria, P. R., & do Nascimento, M. Z. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313101 · Cited by 5

A Deep Learning Framework for Viable Tumor Burden Estimation

Jahromi, S. A. F., Khani, A., Shahreza, H. O., Baghshah, M. S., & Behroozi, H. (2020) · Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS) · doi:10.1109/icspis51611.2020.9349569 · Cited by 5

An Open Source Platform for Computational Histopathology

Yu, X., Zhao, B., Huang, H., Tian, M., Zhang, S., Song, H., Li, Z., Huang, K., & Gao, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3080429 · Cited by 5

Personalized stain style transfer layers for distributed histology classification

Shen, Y. (2022) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2607168 · Cited by 5

Effects of Color Stain Normalization in Histopathology Image Retrieval using Deep Learning

Rinaldi, A. M., Russo, C., & Tommasino, C. (2022) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism55400.2022.00010 · Cited by 5

Colorectal Tissue Image Classification Across Datasets

Plumworasawat, S., & Sae-Bae, N. (2023) · International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) · doi:10.1109/ecti-con58255.2023.10153365 · Cited by 5

Automated high-grade prostate cancer detection and ranking on whole slide images

Huang, C.-H., & Racoceanu, D. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2253642 · Cited by 5

Artificial intelligence methods for predictive image-based grading of human cancers

Fernández, G., Madduri, A. S., Marami, B., Prastawa, M. W., Scott, R., Zeineh, J., & Donovan, M. J. (2020) · Elsevier eBooks · doi:10.1016/b978-0-323-67538-3.00009-9 · Cited by 5

Ensemble Prostate Tumor Classification in H&E Whole Slide Imaging via Stain Normalization and Cell Density Estimation

Weingant, M., Reynolds, H. M., Haworth, A., Mitchell, C., Williams, S., & DiFranco, M. D. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24888-2_34 · Cited by 5

A Convolutional Neural Network Combined with Color Deconvolution for Mitosis Detection

Bai, M., Sun, J., Cheng, K., Luo, J., & Zhao, Y. (2018) · doi:10.1145/3297156.3297168 · Cited by 5

Orientation-Disentangled Unsupervised Representation Learning for Computational PathologyPreprint

Lafarge, M. W., Pluim, J. P. W., & Veta, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.11673 · Cited by 6

Unrolled Projected Gradient Algorithm For Stain Separation In Digital Histopathological Images

Sadraoui, A., Laurent-Bellue, A., Kaaniche, M., Benazza‐Benyahia, A., Guettier, C., & Pesquet, J.-C. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10648171 · Cited by 4

Multi-target Stain Normalization for Histology Slides

Ivanov, D., Barbano, C. A., & Grangetto, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-77786-8_4 · Cited by 4

Deep Learning-Based Multimodal Feature Interaction-Guided Fusion: Enhancing the Evaluation of EGFR in Advanced Lung Adenocarcinoma

Xu, J., Feng, B., Chen, X., Wu, F., Liu, Y., Yu, Z., Lu, S., Duan, X., Chen, X., Li, K., et al. (2025) · Academic Radiology · doi:10.1016/j.acra.2025.04.071 · Cited by 6

Multiscale Fusion Models With Genomic, Topological, and Pathomic Features to Predict Response to Radiation Therapy for Non–Small Cell Lung Cancer Patients

Jin, Y., Arimura, H., Iwasaki, T., Kodama, T., Yamamoto, N., Cui, Y., & Oda, Y. (2025) · Laboratory Investigation · doi:10.1016/j.labinv.2025.104204 · Cited by 4

Deep Learning Methodology for Quantification of Normal Pancreas Structures

Xie, Z., Thibault, S., Shirai, N., Zhan, Y., & Tomlinson, L. (2025) · Toxicologic Pathology · doi:10.1177/01926233251341824 · Cited by 4

Hybrid model for predicting microsatellite instability in colorectal cancer using hematoxylin & eosin-stained images and clinical features

Wei, H., Zhang, X., Zhou, Z., Xie, J., Han, W., & Dong, X. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1580195 · Cited by 4

A position-aware sets based weakly supervised framework for whole-slide subtype classification

Song, J., Yu, B., Liu, X., Cong, L., Zhou, Z., Cong, X., Sun, H., Pang, S., & Chen, H. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111998 · Cited by 4

Multi-cancer analysis of histopathologic MSI screening based on digital histology image

Lee, J., Kim, C. Y., Lee, S., & Chung, J.-H. (2025) · PLoS ONE · doi:10.1371/journal.pone.0332034 · Cited by 5

Self-supervised stain normalization empowers privacy-preserving and model generalization in digital pathology

Wang, J., Yu, J., Yang, H., Zhu, Y., Jiang, L., Li, X., Zhang, J., & Xu, Y. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02196-8 · Cited by 5

A Review of Image‐Based Fabric Color Measurement: Methods, Challenges, and Future Prospects

Nguyen, T. A. (2026) · Color Research & Application · doi:10.1002/col.70060 · Cited by 5

Advancing Content-Based Histopathological Image Retrieval Pre-Processing: A Comparative Analysis of the Effects of Color Normalization Techniques

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Advancing Breast Cancer Prediction and Early Detection with Advanced Deep Learning Models

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