Machine Learning Methods for Histopathological Image Analysis

ImageNet classification with deep convolutional neural networks

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ImageNet Large Scale Visual Recognition Challenge

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A survey on deep learning in medical image analysis

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The cancer genome atlas pan-cancer analysis project

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A comparative study of texture measures with classification based on featured distributions

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Deep learning in medical image analysis

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Solving the multiple instance problem with axis-parallel rectangles

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Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning

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Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)

Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., et al. (2018) · 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) · doi:10.1109/isbi.2018.8363547 · Cited by 2,620

Bilinear CNN Models for Fine-Grained Visual Recognition

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Artificial Intelligence in pathology: current applications, limitations, and future directions

Sajithkumar, A., Thomas, J., Saji, A. M., Ali, F., Haneena Hasin, E. K., Adampulan, H. A. G., & Sarathchand, S. (2023) · Irish Journal of Medical Science (1971 -) · doi:10.1007/s11845-023-03479-3 · Cited by 13

Deep learning approach for accurate prostate cancer identification and stratification using combined immunostaining of cytokeratin, p63, and racemase

Salvi, M., Manini, C., Lopez, J. I., Fenoglio, D., & Molinari, F. (2023) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2023.102288 · Cited by 12

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

Solution-free and simplified H&E staining using a hydrogel-based stamping technology

Kim, J.-H., Choi, W., Yoo, D., Kim, M., Cho, H., Sung, H.-J., Choi, G., Uh, J., Kim, J., Go, H., et al. (2023) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2023.1292785 · Cited by 11

Factors associated with engraftment success of patient-derived xenografts of breast cancer

Lee, J., Lee, G., Park, H. S., Jeong, B.-K., Gong, G., Jeong, J. H., & Lee, H. J. (2024) · Breast Cancer Research · doi:10.1186/s13058-024-01794-w · Cited by 11

Advancements in computer vision and pathology: Unraveling the potential of artificial intelligence for precision diagnosis and beyond

Chang, J., & Hatfield, B. S. (2024) · Advances in cancer research · doi:10.1016/bs.acr.2024.05.006 · Cited by 13

Predicting Metformin Efficacy in Improving Insulin Sensitivity Among Women With Polycystic Ovary Syndrome and Insulin Resistance: A Machine Learning Study

Fu, J., Zhang, Y., Cai, X., & Huang, Y. (2024) · Endocrine Practice · doi:10.1016/j.eprac.2024.07.014 · Cited by 12

Artificial intelligence: A new tool in the pathologist's armamentarium for the diagnosis of IBD

Cannarozzi, A. L., Massimino, L., Latiano, A., Parigi, T. L., Giuliani, F., Bossa, F., Brina, A. L. D., Ungaro, F., Biscaglia, G., Danese, S., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.09.003 · Cited by 11

The role of histological stains in aquaculture and fisheries development technology

Eissa, E. H., Gaafar, A. Y., Mathew, R. T., Al-Shahari, E. A., Okon, E. M., Eissa, M. E. H., & El‐Aziz, Y. M. A. (2025) · Aquaculture International · doi:10.1007/s10499-025-01932-x · Cited by 11

Deep Learning-Based Drug Screening for COVID-19 and Case Studies

Saravanan, K. M., Zhang, H., Hossain, M. T., Reza, M. S., & Wei, Y. (2021) · Methods in pharmacology and toxicology · doi:10.1007/7653_2020_58 · Cited by 13

New bag-of-feature for histopathology image classification using reinforced cat swarm algorithm and weighted Gaussian mixture modelling

Vijh, S., Kumar, S., & Saraswat, M. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00726-5 · Cited by 11

The use of generative adversarial networks for multi-site one-class follicular lymphoma classification

Somaratne, U. V., Wong, K. W., Parry, J., & Laga, H. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08810-8 · Cited by 12

Adversarial-Based Domain Adaptation Networks for Unsupervised Tumour Detection in Histopathology

Figueira, G., Wang, Y., Sun, L., Zhou, H., & Zhang, Q. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098699 · Cited by 11

Recurrent neural network ensemble, a new instrument for the prediction of infectious diseases

Puleio, A. (2021) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-021-01285-3 · Cited by 11

Personalized Cell Therapy for Patients with Peripheral Arterial Diseases in the Context of Genetic Alterations: Artificial Intelligence-Based Responder and Non-Responder Prediction

Salybekov, A. A., Wolfien, M., Kobayashi, S., Steinhoff, G., & Asahara, T. (2021) · Cells · doi:10.3390/cells10123266 · Cited by 10

Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological imagesPreprint

Yu, G., Xie, T., Xu, C., Shi, X. M., Wu, C., Sun, K., Meng, R.-Q., Meng, X., Wang, K.-S., Xiao, H., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.13.201582 · Cited by 11

An interpretable hybrid deep learning framework for gastric cancer diagnosis using histopathological imaging

Ren, T., Govindarajan, V., Bourouis, S., Wang, X., & Ke, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-15702-5 · Cited by 12

Building Efficient CNN Architectures for Histopathology Images Analysis: A Case-Study in Tumor-Infiltrating Lymphocytes Classification

Meirelles, A. L. S., Kurç, T., Kong, J., Ferreira, R., Saltz, J. H., & Teodoro, G. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.894430 · Cited by 12

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

Pedraza, A., Gonzalez, L., Déniz, Ó., & Bueno, G. (2024) · Algorithms · doi:10.3390/a17030097 · Cited by 10

Ovarian cancer data analysis using deep learning: A systematic review

Hira, M. T., Razzaque, M. A., & Sarker, M. H. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109250 · Cited by 27

Label-free Aβ plaque detection in Alzheimer's disease brain tissue using infrared microscopy and neural networks

Müller, D., Röhr, D., Boon, B. D. C., Wulf, M., Arto, T., Hoozemans, J. J. M., Marcus, K., Rozemüller, A. J. M., Großerueschkamp, F., Axel Mosig, A., et al. (2025) · Heliyon · doi:10.1016/j.heliyon.2025.e42111 · Cited by 10

GLMKD: Joint global and local mutual knowledge distillation for weakly supervised lesion segmentation in histopathology images

Cheng, H., Liu, X., Zhang, J., Dong, X., Ma, X., Zhang, Y., Meng, H., Chen, X., Yue, G., Li, Y., et al. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127425 · Cited by 10

DetexNet: Accurately Diagnosing Frequent and Challenging Pediatric Malignant Tumors

Liu, Y., Yin, M., & Sun, S. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3027547 · Cited by 10

Deep Learning for Histopathological Image Analysis

Wemmert, C., Weber, J., Feuerhake, F., & Forestier, G. (2021) · Springer eBooks · doi:10.1007/978-3-030-71676-9_7 · Cited by 11

Using parallel pre-trained types of DCNN model to predict breast cancer with color normalization

Noumah, W. A., Jafar, A., & Joumaa, K. A. (2022) · BMC Research Notes · doi:10.1186/s13104-021-05902-3 · Cited by 10

Multiscale generative model using regularized skip-connections and perceptual loss for anomaly detection in toxicologic histopathology

Zehnder, P., Feng, J., Fuji, R. N., Sullivan, R., & Hu, F. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100102 · 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

Lightweight multi-scale classification of chest radiographs via size-specific batch normalization

Pereira, S. C., Rocha, J., Campilho, A., Sousa, P., & Mendonça, A. M. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107558 · Cited by 11

Recent ViT based models for Breast Cancer Histopathology Image Classification

Karuppasamy, A. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10306739 · Cited by 11

Deep learning networks reflect cytoarchitectonic features used in brain mapping

Kiwitz, K., Schiffer, C., Spitzer, H., Dickscheid, T., & Amunts, K. (2020) · Scientific Reports · doi:10.1038/s41598-020-78638-y · 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

Deep-Fuzz: A synergistic integration of deep learning and fuzzy water flows for fine-grained nuclei segmentation in digital pathology

Das, N., Saha, S., Nasipuri, M., Basu, S., & Chakraborti, T. (2023) · PLoS ONE · doi:10.1371/journal.pone.0286862 · Cited by 10

Clinical Decision Support for Ovarian Carcinoma Subtype Classification: A Pilot Observer Study With Pathology Trainees

Gavrielides, M. A., Miller, M., Hagemann, I. S., Abdelal, H. A., Alipour, Z., Chen, J., Salari, B., Sun, L., Zhou, H., & Seidman, J. D. (2019) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2019-0390-oa · Cited by 10

A hybrid unsupervised—Deep learning tandem for electrooculography time series analysis

Stoean, R., Stoean, C., Becerra-García, R., García−Bermúdez, R., Atencia, M., García-Lagos, F., Velázquez‐Pérez, L., & Joya, G. (2020) · PLoS ONE · doi:10.1371/journal.pone.0236401 · Cited by 10

Discriminative Subtyping of Lung Cancers from Histopathology Images via Contextual Deep LearningPreprint

Lengerich, B. J., Al-Shedivat, M., Alavi, A., Williams, J. A., Labbaki, S., & Xing, E. P. (2020) · medRxiv · doi:10.1101/2020.06.25.20140053 · Cited by 10

Editorial: Interpretable and explainable machine learning models in oncology

Hrinivich, W. T., Wang, T., & Wang, C. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1184428 · Cited by 15

Magnetic resonance imaging and ultrasound elastography in the context of preclinical pharmacological research: significance for the 3R principles

Obrecht, M., Zurbruegg, S., Accart, N., Lambert, C., Doelemeyer, A., Ledermann, B., & Beckmann, N. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1177421 · Cited by 9

Analysis of cellularity in H&E-stained rat bone marrow tissue via deep learning

Shiffman, S., Piedra, E. A. R., Adedeji, A. O., Ruff, C. F., Andrews, R. N., Katavolos, P., Liu, E., Forster, A., Brumm, J., Fuji, R. N., et al. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100333 · Cited by 9

Advancements in Cardiotoxicity Detection and Assessment through Artificial Intelligence: A Comprehensive Review

Ahmed, B., Daaif, A., & Bousselham, A. (2024) · International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · doi:10.1109/iraset60544.2024.10548722 · Cited by 10

Development of a multi‐scanner facility for data acquisition for digital pathology artificial intelligence

Humphries, M. P., Kaye, D., Stankeviciute, G., Halliwell, J., Wright, A. I., Bansal, D., Brettle, D. S., & Treanor, D. E. (2024) · The Journal of Pathology · doi:10.1002/path.6326 · Cited by 9

NMGrad: Advancing Histopathological Bladder Cancer Grading with Weakly Supervised Deep Learning

Fuster, S., Kiraz, U., Eftestøl, T. C., Janssen, E. A. M., & Engan, K. (2024) · Bioengineering · doi:10.3390/bioengineering11090909 · Cited by 9

A first explainable-AI-based workflow integrating forward-forward and backpropagation-trained networks of label-free multiphoton microscopy images to assess human biopsies of rare neuromuscular disease

Scodellaro, R., Zschüntzsch, J., Hell, A. K., & Alves, F. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108733 · Cited by 11

Artificial intelligence in pancreatic cancer histopathology and diagnostics - implications for clinical decisions and biomarker discovery?

Weselá, P., Eid, M., Moravčík, P., Vlažný, J., Hlavsa, J., Procházka, V., Kala, Z., & Vaňhara, P. (2025) · Cell Division · doi:10.1186/s13008-025-00158-w · Cited by 10

Autoencoder-Assisted Stacked Ensemble Learning for Lymphoma Subtype Classification: A Hybrid Deep Learning and Machine Learning Approach

Ogundokun, R. O., Owolawi, P. A., Tu, C., & Wyk, E. V. (2025) · Tomography · doi:10.3390/tomography11080091 · Cited by 9

Model Fooling Attacks Against Medical Imaging: A Short Survey

Sipola, T., Puuska, S., & Kokkonen, T. (2020) · Information & Security An International Journal · doi:10.11610/isij.4615 · Cited by 10

Universal Convolutional Neural Network for Histology-Independent Analysis of Collagen Fiber Organization in Scar Tissue

Pham, T. T. A., Kim, H., Lee, Y., Kang, H. W., & Park, S. (2022) · IEEE Access · doi:10.1109/access.2022.3162272 · Cited by 9

Effective Classification of Colon Cancer using Resnet-18 in Comparison with Squeezenet

Vasu, K., & Kumar, S. P. (2022) · Journal of Pharmaceutical Negative Results · doi:10.47750/pnr.2022.13.s04.169 · Cited by 9

Prostate Cancer Grading Using Multistage Deep Neural Networks

Bygari, R., Rithesh, K., Ambesange, S., & Koolagudi, S. G. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-5868-7_21 · Cited by 9

Code-free machine learning for classification of central nervous system histopathology images

Jungo, P., & Hewer, E. (2023) · Journal of Neuropathology & Experimental Neurology · doi:10.1093/jnen/nlac131 · Cited by 9

Toxicity of Atmospheric Aerosols: Methodologies & Assays

Liu, F., & Ng, N. L. (2023) · ACS in focus · doi:10.1021/acsinfocus.7e7012 · Cited by 9

Collagen IV and laminin‐1 expression in embryonic mouse lens using principal components analysis technique

Sijilmassi, O., Alonso, J. M. L., ASENSIO, M. C. B., & Sevilla, A. D. R. (2018) · Journal of Microscopy · doi:10.1111/jmi.12709 · Cited by 9

A multiscale Mueller polarimetry module for a stereo zoom microscope

Gribble, A., Pinkert, M. A., Westreich, J., Liu, Y., Keikhosravi, A., Khorasani, M., Nofech‐Mozes, S., Eliceiri, K. W., & Vitkin, I. A. (2019) · Biomedical Engineering Letters · doi:10.1007/s13534-019-00116-w · Cited by 9

Cancer Detection in Breast Histopathology with Convolution Neural Network Based Approach

Kausar, T., Wang, M., & Malik, M. S. S. (2019) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa47632.2019.9035244 · Cited by 9

Cross-Domain Knowledge Transfer for Prediction of Chemosensitivity in Ovarian Cancer Patients

Yaar, A., Asif, A., Raza, S. E. A., Rajpoot, N., & Minhas, F. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00472 · Cited by 9

PMNet: A probability map based scaled network for breast cancer diagnosis

Ahmed, S., Tariq, M., & Naveed, H. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101863 · Cited by 9

Exploring Histological Similarities Across Cancers From a Deep Learning Perspective

Menon, A., Singh, P. K., Vinod, P. K. U., & Jawahar, C. V. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.842759 · Cited by 9

Deep Learning for Biomedical Data Analysis

(2021) · Springer eBooks · doi:10.1007/978-3-030-71676-9 · Cited by 10

Discriminative Dictionary Learning Using Penalized Rank-1 Approximation for Breast Cancer Classification With Imbalanced Dataset

Haider, U., Hanif, M. I., Rashid, A., Aurangzeb, K., Khalil, A., & Alhussein, M. A. (2023) · IEEE Access · doi:10.1109/access.2023.3347339 · Cited by 8

Multimodal Fusion Framework Based on Low-Rank Interaction for Tumor Prognostic Prediction

An, Y., Lan, R., Lin, H., Wang, Y., Deng, H., Liu, Z., Wang, L., Liu, Z., Lu, C. Y., Yang, H., et al. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3578334 · Cited by 8

On Supporting Cancer Grading Based on Histological Slides Using a Limited Number of Features

Boruz, D. B. M., & Stoean, C. (2018) · Annals of the University of Craiova Mathematics and Computer Science Series · Cited by 8

Histopathological Colorectal Cancer Image Classification by Using Inception V4 CNN Model

Patnaik, R. K., Rath, P. S., Padhy, S. K., & Dash, S. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-4634-1_79 · Cited by 9

A Histopathologic Image Analysis for the Classification of Endocervical Adenocarcinoma Silva Patterns Depend on Weakly Supervised Deep Learning

Liu, Q., Zhang, X., Jiang, X., Zhang, C., Li, J., Zhang, X., Yang, J., Yu, N., Zhu, Y., Liu, J., et al. (2024) · American Journal Of Pathology · doi:10.1016/j.ajpath.2024.01.016 · Cited by 8

Prompting Vision Foundation Models for Pathology Image Analysis

Yin, C., Liu, S., Zhou, K., Wong, V. W. S., & Yuen, P. C. (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/cvpr52733.2024.01073 · Cited by 8

Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology

Guichemerre, A., Belharbi, S., Mayet, T., Murtaza, S., Shamsolmoali, P., McCaffrey, L. M., & Granger, É. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00008 · Cited by 8

High-Resolution Generative Adversarial Neural Networks Applied to Histological Images Generation

Mauricio, A., López, J., Huauya, R., & Díaz, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01421-6_20 · Cited by 8

A Review of Intrinsic Optical Imaging Serial Blockface Histology (ICI-SBH) for Whole Rodent Brain Imaging

Lefebvre, J., Delafontaine-Martel, P., & Lesage, F. (2019) · Photonics · doi:10.3390/photonics6020066 · Cited by 8

Morphological Estimation of Cellularity on Neo-Adjuvant Treated Breast Cancer Histological Images

Ortega-Ruíz, M. A., Karabağ, C., Garduño, V. G., & Reyes‐Aldasoro, C. C. (2020) · Journal of Imaging · doi:10.3390/jimaging6100101 · Cited by 9

Dual Stage Normalization Approach Towards Classification of Breast Cancer

A., A. M., & Jagannath, M. (2020) · IETE Journal of Research · doi:10.1080/03772063.2020.1754140 · Cited by 9

Improving prostate cancer classification in H&E tissue micro arrays using Ki67 and P63 histopathology

Shao, Y., Nir, G., Fazli, L., Goldenberg, L., Gleave, M. E., Black, P. C., Wang, J., & Salcudean, S. E. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104053 · Cited by 8

Machine Learning Methods for Histopathological Image Analysis: A Review

de Matos, J., Ataky, S. T. M., Britto, A. D. S., Oliveira, L. S., & Koerich, A. L. (2021) · Electronics · doi:10.3390/electronics10050562 · Cited by 14

A Convolutional Neural Network Approach for Detecting Malignancy of Ovarian Cancer

Mathur, M., & Jindal, V. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-1249-7_29 · Cited by 8

An Ensemble Learning Method Based on an Evidential Reasoning Rule considering Combination Weighting

Xu, C., Zhang, Y., Zhang, W., Zu, H., Zhang, Y., & He, W. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/1156748 · Cited by 8

Intelligent framework for brain tumor grading using advanced feature analysis

Mohan, G., & Subashini, M. M. (2022) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2022.2083017 · Cited by 8

Preparing pathological data to develop an artificial intelligence model in the nonclinical study

Hwang, J., Lim, M., Han, G., Park, H., Kim, Y., Park, J., Jun, S.-Y., Lee, J., & Cho, J. (2023) · Scientific Reports · doi:10.1038/s41598-023-30944-x · Cited by 8

A Survey on Application of Machine Learning Algorithms in Cancer Prediction and Prognosis

Deepti, & Ray, S. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-5616-6_25 · Cited by 8

Semi-supervised Learning with a Teacher-Student Paradigm for Histopathology Classification: A Resource to Face Data Heterogeneity and Lack of Local Annotations

Marini, N., Otálora, S., Müller, H., & Atzori, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68763-2_9 · Cited by 8

Plant leaf deep semantic segmentation and a novel benchmark dataset for morning glory plant harvesting

Su, J., Anderson, S., Javed, M., Khompatraporn, C., Udomsakdigool, A., & Mihaylova, L. S. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126609 · Cited by 7

Enhancing chromosomal analysis efficiency through deep learning-based artificial intelligence graphic analysis

Zhou, Y., Xu, L., Zhang, L., Shi, D., Wu, C., Wei, R., Song, N., Wu, S., Chen, C., & Li, H. (2024) · Discover Applied Sciences · doi:10.1007/s42452-024-05980-5 · Cited by 7

A moment-based pooling approach in convolutional neural networks for breast cancer histopathology image classification

Kumar, A., Singh, C., & Sachan, M. K. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10406-9 · Cited by 8

Artificial Intelligence Applications in Lymphoma Diagnosis and Management: Opportunities, Challenges, and Future Directions

Shen, M., & Jiang, Z. (2024) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s485724 · Cited by 7

Artificial intelligence and whole slide imaging, a new tool for the microsatellite instability prediction in colorectal cancer: Friend or foe?

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Digital Pathology Tailored for Assessment of Liver Biopsies

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