{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T23:26:31Z","timestamp":1782170791269,"version":"3.54.5"},"reference-count":196,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T00:00:00Z","timestamp":1739577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"\u201cRADIATIONS\u201d under the PNRR\u2014Next Generation EU, Mission 4, Component 2\u2014Investment 1.5\u2014Cascading Call\u2014Ecosystem of Innovation \u201cTHE\u2014Tuscany Health Ecosystem\u201d","award":["ECS00000017\u2014CUP B83C22003930001"],"award-info":[{"award-number":["ECS00000017\u2014CUP B83C22003930001"]}]},{"name":"\u201cRADIATIONS\u201d under the PNRR\u2014Next Generation EU, Mission 4, Component 2\u2014Investment 1.5\u2014Cascading Call\u2014Ecosystem of Innovation \u201cTHE\u2014Tuscany Health Ecosystem\u201d","award":["PNC-E3-2022-23681266 PNCHTS-DA 1"],"award-info":[{"award-number":["PNC-E3-2022-23681266 PNCHTS-DA 1"]}]},{"name":"\u201cRADIATIONS\u201d under the PNRR\u2014Next Generation EU, Mission 4, Component 2\u2014Investment 1.5\u2014Cascading Call\u2014Ecosystem of Innovation \u201cTHE\u2014Tuscany Health Ecosystem\u201d","award":["CUP D73C22OO2090001"],"award-info":[{"award-number":["CUP D73C22OO2090001"]}]},{"name":"\u201cRADIATIONS\u201d under the PNRR\u2014Next Generation EU, Mission 4, Component 2\u2014Investment 1.5\u2014Cascading Call\u2014Ecosystem of Innovation \u201cTHE\u2014Tuscany Health Ecosystem\u201d","award":["W911NF-22-1-0267"],"award-info":[{"award-number":["W911NF-22-1-0267"]}]},{"name":"\u201cINNOVA\u201d\u2014Advanced Diagnostic, Project promoted by Italian Ministry of Health","award":["ECS00000017\u2014CUP B83C22003930001"],"award-info":[{"award-number":["ECS00000017\u2014CUP B83C22003930001"]}]},{"name":"\u201cINNOVA\u201d\u2014Advanced Diagnostic, Project promoted by Italian Ministry of Health","award":["PNC-E3-2022-23681266 PNCHTS-DA 1"],"award-info":[{"award-number":["PNC-E3-2022-23681266 PNCHTS-DA 1"]}]},{"name":"\u201cINNOVA\u201d\u2014Advanced Diagnostic, Project promoted by Italian Ministry of Health","award":["CUP D73C22OO2090001"],"award-info":[{"award-number":["CUP D73C22OO2090001"]}]},{"name":"\u201cINNOVA\u201d\u2014Advanced Diagnostic, Project promoted by Italian Ministry of Health","award":["W911NF-22-1-0267"],"award-info":[{"award-number":["W911NF-22-1-0267"]}]},{"name":"Army Research Office","award":["ECS00000017\u2014CUP B83C22003930001"],"award-info":[{"award-number":["ECS00000017\u2014CUP B83C22003930001"]}]},{"name":"Army Research Office","award":["PNC-E3-2022-23681266 PNCHTS-DA 1"],"award-info":[{"award-number":["PNC-E3-2022-23681266 PNCHTS-DA 1"]}]},{"name":"Army Research Office","award":["CUP D73C22OO2090001"],"award-info":[{"award-number":["CUP D73C22OO2090001"]}]},{"name":"Army Research Office","award":["W911NF-22-1-0267"],"award-info":[{"award-number":["W911NF-22-1-0267"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Artificial intelligence (AI) transforms image data analysis across many biomedical fields, such as cell biology, radiology, pathology, cancer biology, and immunology, with object detection, image feature extraction, classification, and segmentation applications. Advancements in deep learning (DL) research have been a critical factor in advancing computer techniques for biomedical image analysis and data mining. A significant improvement in the accuracy of cell detection and segmentation algorithms has been achieved as a result of the emergence of open-source software and innovative deep neural network architectures. Automated cell segmentation now enables the extraction of quantifiable cellular and spatial features from microscope images of cells and tissues, providing critical insights into cellular organization in various diseases. This review aims to examine the latest AI and DL techniques for cell analysis and data mining in microscopy images, aid the biologists who have less background knowledge in AI and machine learning (ML), and incorporate the ML models into microscopy focus images.<\/jats:p>","DOI":"10.3390\/jimaging11020059","type":"journal-article","created":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T11:31:17Z","timestamp":1739791877000},"page":"59","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7913-704X","authenticated-orcid":false,"given":"Muhammad","family":"Ali","sequence":"first","affiliation":[{"name":"Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy"},{"name":"Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3629-355X","authenticated-orcid":false,"given":"Viviana","family":"Benfante","sequence":"additional","affiliation":[{"name":"Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy"},{"name":"Advanced Diagnostic Imaging\u2014INNOVA Project, Department of Radiological Sciences, A.R.N.A.S. Civico, Di Cristina e Benfratelli Hospitals, P.zza N. Leotta 4, 90127 Palermo, Italy"},{"name":"Pharmaceutical Factory, La Maddalena S.P.A., Via San Lorenzo Colli, 312\/d, 90146 Palermo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ghazal","family":"Basirinia","sequence":"additional","affiliation":[{"name":"Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy"},{"name":"Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8397-1640","authenticated-orcid":false,"given":"Pierpaolo","family":"Alongi","sequence":"additional","affiliation":[{"name":"Advanced Diagnostic Imaging\u2014INNOVA Project, Department of Radiological Sciences, A.R.N.A.S. Civico, Di Cristina e Benfratelli Hospitals, P.zza N. Leotta 4, 90127 Palermo, Italy"},{"name":"Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, 90127 Palermo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Sperandeo","sequence":"additional","affiliation":[{"name":"Pharmaceutical Factory, La Maddalena S.P.A., Via San Lorenzo Colli, 312\/d, 90146 Palermo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7034-4915","authenticated-orcid":false,"given":"Alberto","family":"Quattrocchi","sequence":"additional","affiliation":[{"name":"Pathologic Anatomy Unit, Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, 90127 Palermo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1219-627X","authenticated-orcid":false,"given":"Antonino Giulio","family":"Giannone","sequence":"additional","affiliation":[{"name":"Pathologic Anatomy Unit, Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, 90127 Palermo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6571-6577","authenticated-orcid":false,"given":"Daniela","family":"Cabibi","sequence":"additional","affiliation":[{"name":"Pathologic Anatomy Unit, Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, 90127 Palermo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthony","family":"Yezzi","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4529-3703","authenticated-orcid":false,"given":"Domenico","family":"Di Raimondo","sequence":"additional","affiliation":[{"name":"Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6440-7318","authenticated-orcid":false,"given":"Antonino","family":"Tuttolomondo","sequence":"additional","affiliation":[{"name":"Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9290-6103","authenticated-orcid":false,"given":"Albert","family":"Comelli","sequence":"additional","affiliation":[{"name":"Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Segeritz, C.-P., and Vallier, L. (2017). Cell Culture. Basic Science Methods for Clinical Researchers, Elsevier.","DOI":"10.1016\/B978-0-12-803077-6.00009-6"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2202936","DOI":"10.1002\/adhm.202202936","article-title":"Recent Advances on Cell Culture Platforms for In Vitro Drug Screening and Cell Therapies: From Conventional to Microfluidic Strategies","volume":"12","author":"Cardoso","year":"2023","journal-title":"Adv. Healthc. Mater."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ali, M., Benfante, V., Di Raimondo, D., Salvaggio, G., Tuttolomondo, A., and Comelli, A. (2020). Recent Developments in Nanoparticle Formulations for Resveratrol Encapsulation as an Anticancer Agent. Pharmaceuticals, 17.","DOI":"10.3390\/ph17010126"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Dolskiy, A.A., Grishchenko, I.V., and Yudkin, D.V. (2020). Cell Cultures for Virology: Usability, Advantages, and Prospects. Int. J. Mol. Sci., 21.","DOI":"10.3390\/ijms21217978"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.nbt.2023.11.003","article-title":"Manipulating Gene Expression Levels in Mammalian Cell Factories: An Outline of Synthetic Molecular Toolboxes to Achieve Multiplexed Control","volume":"79","author":"Eisenhut","year":"2024","journal-title":"New Biotechnol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Di Baldassarre, A., Cimetta, E., Bollini, S., Gaggi, G., and Ghinassi, B. (2018). Human-Induced Pluripotent Stem Cell Technology and Cardiomyocyte Generation: Progress and Clinical Applications. Cells, 7.","DOI":"10.3390\/cells7060048"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ali, M., Benfante, V., Stefano, A., Yezzi, A., Di Raimondo, D., Tuttolomondo, A., and Comelli, A. (2023). Anti-Arthritic and Anti-Cancer Activities of Polyphenols: A Review of the Most Recent In Vitro Assays. Life, 13.","DOI":"10.3390\/life13020361"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Benfante, V., Stefano, A., Comelli, A., Giaccone, P., Cammarata, F.P., Richiusa, S., Scopelliti, F., Pometti, M., Ficarra, M., and Cosentino, S. (2022). A New Preclinical Decision Support System Based on PET Radiomics: A Preliminary Study on the Evaluation of an Innovative 64Cu-Labeled Chelator in Mouse Models. J. Imaging, 8.","DOI":"10.3390\/jimaging8040092"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Basirinia, G., Ali, M., Comelli, A., Sperandeo, A., Piana, S., Alongi, P., Longo, C., Di Raimondo, D., Tuttolomondo, A., and Benfante, V. (2024). Theranostic Approaches for Gastric Cancer: An Overview of In Vitro and In Vivo Investigations. Cancers, 16.","DOI":"10.3390\/cancers16193323"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ali, M., Benfante, V., Di Raimondo, D., Laudicella, R., Tuttolomondo, A., and Comelli, A. (2024). A Review of Advances in Molecular Imaging of Rheumatoid Arthritis: From In Vitro to Clinic Applications Using Radiolabeled Targeting Vectors with Technetium-99m. Life, 14.","DOI":"10.3390\/life14060751"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Benfante, V., Stefano, A., Ali, M., Laudicella, R., Arancio, W., Cucchiara, A., Caruso, F., Cammarata, F.P., Coronnello, C., and Russo, G. (2023). An Overview of In Vitro Assays of 64Cu-, 68Ga-, 125I-, and 99mTc-Labelled Radiopharmaceuticals Using Radiometric Counters in the Era of Radiotheranostics. Diagnostics, 13.","DOI":"10.3390\/diagnostics13071210"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.btre.2015.04.004","article-title":"Validation of Three Viable-Cell Counting Methods: Manual, Semi-Automated, and Automated","volume":"7","year":"2015","journal-title":"Biotechnol. Rep."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.1007\/s10895-018-2306-4","article-title":"The Detailed Comparison of Cell Death Detected by Annexin V-PI Counterstain Using Fluorescence Microscope, Flow Cytometry and Automated Cell Counter in Mammalian and Microalgae Cells","volume":"28","author":"Uzuner","year":"2018","journal-title":"J. Fluoresc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1748","DOI":"10.1038\/s41563-024-01971-4","article-title":"Substrates Mimicking the Blastocyst Geometry Revert Pluripotent Stem Cell to Naivety","volume":"23","author":"Xu","year":"2024","journal-title":"Nat. Mater."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1002\/jctb.4447","article-title":"Application of Process Analytical Technology for Downstream Purification of Biotherapeutics","volume":"90","author":"Rathore","year":"2015","journal-title":"J. Chem. Technol. Biotechnol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1002\/bit.22529","article-title":"Process Analytical Technology (PAT) for Biopharmaceutical Products: Part II. Concepts and Applications","volume":"105","author":"Read","year":"2010","journal-title":"Biotechnol. Bioeng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jan, M., Spangaro, A., Lenartowicz, M., and Mattiazzi Usaj, M. (2024). From Pixels to Insights: Machine Learning and Deep Learning for Bioimage Analysis. BioEssays, 46.","DOI":"10.1002\/bies.202300114"},{"key":"ref_18","first-page":"100179","article-title":"Artificial Intelligence: A Powerful Paradigm for Scientific Research","volume":"2","author":"Xu","year":"2021","journal-title":"Innovation"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/s12551-022-00949-3","article-title":"Deep Learning-Based Image Processing in Optical Microscopy","volume":"14","author":"Melanthota","year":"2022","journal-title":"Biophys. Rev."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Masud, N., Rade, J., Hasib, M.H., Krishnamurthy, A., and Sarkar, A. (2024). Machine Learning Approaches for Improving Atomic Force Microscopy Instrumentation and Data Analytics. Front. Phys., 12.","DOI":"10.3389\/fphy.2024.1347648"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1016\/j.cmi.2020.02.006","article-title":"Machine Learning in the Clinical Microbiology Laboratory: Has the Time Come for Routine Practice?","volume":"26","author":"Rodriguez","year":"2020","journal-title":"Clin. Microbiol. Infect."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1093\/postmj\/qgad095","article-title":"Computer Image Analysis with Artificial Intelligence: A Practical Introduction to Convolutional Neural Networks for Medical Professionals","volume":"99","author":"Kourounis","year":"2023","journal-title":"Postgrad. Med. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1002\/cyto.a.23701","article-title":"Deep Learning in Image Cytometry: A Review","volume":"95","author":"Gupta","year":"2019","journal-title":"Cytom. Part A"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Valente, J., Ant\u00f3nio, J., Mora, C., and Jardim, S. (2023). Developments in Image Processing Using Deep Learning and Reinforcement Learning. J. Imaging, 9.","DOI":"10.3390\/jimaging9100207"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zidane, M., Makky, A., Bruhns, M., Rochwarger, A., Babaei, S., Claassen, M., and Sch\u00fcrch, C.M. (2023). A Review on Deep Learning Applications in Highly Multiplexed Tissue Imaging Data Analysis. Front. Bioinform., 3.","DOI":"10.3389\/fbinf.2023.1159381"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/JPROC.2019.2949575","article-title":"Deep-Learning-Based Image Reconstruction and Enhancement in Optical Microscopy","volume":"108","author":"Rivenson","year":"2020","journal-title":"Proc. IEEE"},{"key":"ref_27","unstructured":"Mazzeo, P.L., Frontoni, E., Sclaroff, S., and Distante, C. (2022). PET Images Atlas-Based Segmentation Performed in Native and in Template Space: A Radiomics Repeatability Study in Mouse Models. Image Analysis and Processing. ICIAP 2022 Workshops, Springer International Publishing."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mazzeo, P.L., Frontoni, E., Sclaroff, S., and Distante, C. (2022). Robustness of Radiomics Features to Varying Segmentation Algorithms in Magnetic Resonance Images. Image Analysis and Processing. ICIAP 2022 Workshops, Springer International Publishing.","DOI":"10.1007\/978-3-031-13324-4"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mazzeo, P.L., Frontoni, E., Sclaroff, S., and Distante, C. (2022). A Predictive System to Classify Preoperative Grading of Rectal Cancer Using Radiomics Features. Image Analysis and Processing. ICIAP 2022 Workshops, Springer International Publishing.","DOI":"10.1007\/978-3-031-13324-4"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1038\/s41374-020-00514-0","article-title":"Artificial Intelligence and Computational Pathology","volume":"101","author":"Cui","year":"2021","journal-title":"Lab. Investig."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1158\/2159-8290.CD-21-0090","article-title":"Artificial Intelligence in Cancer Research and Precision Medicine","volume":"11","author":"Bhinder","year":"2021","journal-title":"Cancer Discov."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.2147\/JMDH.S410301","article-title":"Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach","volume":"16","author":"Zhang","year":"2023","journal-title":"JMDH"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"9861263","DOI":"10.34133\/2022\/9861263","article-title":"Deep Learning in Cell Image Analysis","volume":"2022","author":"Xu","year":"2022","journal-title":"Intell. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1016\/j.ajpath.2021.05.022","article-title":"Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images","volume":"191","author":"Durkee","year":"2021","journal-title":"Am. J. Pathol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"157","DOI":"10.4103\/digm.digm_16_18","article-title":"Biological Image Analysis Using Deep Learning-Based Methods: Literature Review","volume":"4","author":"Wang","year":"2018","journal-title":"Digit. Med."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"104504","DOI":"10.1016\/j.engappai.2021.104504","article-title":"Machine Learning Applications in Power System Fault Diagnosis: Research Advancements and Perspectives","volume":"106","author":"Vaish","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Alongi, P., Stefano, A., Comelli, A., Spataro, A., Formica, G., Laudicella, R., Lanzafame, H., Panasiti, F., Longo, C., and Midiri, F. (2022). Artificial Intelligence Applications on Restaging [18F]FDG PET\/CT in Metastatic Colorectal Cancer: A Preliminary Report of Morpho-Functional Radiomics Classification for Prediction of Disease Outcome. Appl. Sci., 12.","DOI":"10.3390\/app12062941"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s13534-020-00179-0","article-title":"Deep Learning Approach for the Segmentation of Aneurysmal Ascending Aorta","volume":"11","author":"Comelli","year":"2021","journal-title":"Biomed. Eng. Lett."},{"key":"ref_39","first-page":"525","article-title":"AI-Enabled Organoids: Construction, Analysis, and Application","volume":"31","author":"Bai","year":"2024","journal-title":"Bioact. Mater."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Alsulimani, A., Akhter, N., Jameela, F., Ashgar, R.I., Jawed, A., Hassani, M.A., and Dar, S.A. (2024). The Impact of Artificial Intelligence on Microbial Diagnosis. Microorganisms, 12.","DOI":"10.3390\/microorganisms12061051"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Sebastian, A.M., and Peter, D. (2022). Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions. Life, 12.","DOI":"10.3390\/life12121991"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Foresti, G.L., Fusiello, A., and Hancock, E. (2024). Prostate Cancer Detection: Performance of Radiomics Analysis in Multiparametric MRI. Image Analysis and Processing\u2014ICIAP 2023 Workshops, Springer Nature Switzerland.","DOI":"10.1007\/978-3-031-51026-7"},{"key":"ref_43","unstructured":"Foresti, G.L., Fusiello, A., and Hancock, E. (2024). Grading and Staging of Bladder Tumors Using Radiomics Analysis in Magnetic Resonance Imaging. Image Analysis and Processing\u2014ICIAP 2023 Workshops, Springer Nature Switzerland."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Corso, R., Stefano, A., Salvaggio, G., and Comelli, A. (2024). Shearlet Transform Applied to a Prostate Cancer Radiomics Analysis on MR Images. Mathematics, 12.","DOI":"10.3390\/math12091296"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e33208","DOI":"10.1016\/j.heliyon.2024.e33208","article-title":"Hyperspectral Imaging and Its Applications: A Review","volume":"10","author":"Bhargava","year":"2024","journal-title":"Heliyon"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Mukundan, A., Liu, Y.-S., Tsao, Y.-M., Lin, F.-C., Fan, W.-S., and Wang, H.-C. (2023). Optical Identification of Diabetic Retinopathy Using Hyperspectral Imaging. J. Pers. Med., 13.","DOI":"10.3390\/jpm13060939"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"470","DOI":"10.7150\/jca.102759","article-title":"Evaluation of Band Selection for Spectrum-Aided Visual Enhancer (SAVE) for Esophageal Cancer Detection","volume":"16","author":"Chen","year":"2025","journal-title":"J. Cancer"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Liu, R., Dai, W., Wu, T., Wang, M., Wan, S., and Liu, J. (2022). AIMIC: Deep Learning for Microscopic Image Classification. Comput. Methods Programs Biomed., 226.","DOI":"10.1016\/j.cmpb.2022.107162"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1177\/2472555216682725","article-title":"Biologically Relevant Heterogeneity: Metrics and Practical Insights","volume":"22","author":"Gough","year":"2017","journal-title":"SLAS Discov."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.gendis.2022.11.025","article-title":"Cancer Cell Cycle Heterogeneity as a Critical Determinant of Therapeutic Resistance","volume":"11","author":"Maleki","year":"2024","journal-title":"Genes. Dis."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/B978-0-12-391857-4.00019-7","article-title":"Monitoring Protein Interactions in Living Cells with Fluorescence Lifetime Imaging Microscopy","volume":"Volume 504","author":"Sun","year":"2012","journal-title":"Methods in Enzymology"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"101748","DOI":"10.1016\/j.xpro.2022.101748","article-title":"Assessment of Protein Inclusions in Cultured Cells Using Automated Image Analysis","volume":"3","author":"McAlary","year":"2022","journal-title":"STAR Protoc."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1038\/s41416-024-02882-6","article-title":"Spatial Analysis by Current Multiplexed Imaging Technologies for the Molecular Characterisation of Cancer Tissues","volume":"131","author":"Semba","year":"2024","journal-title":"Br. J. Cancer"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Carreras-Puigvert, J., and Spjuth, O. (2024). Artificial Intelligence for High Content Imaging in Drug Discovery. Curr. Opin. Struct. Biol., 87.","DOI":"10.1016\/j.sbi.2024.102842"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Pearson, Y.E., Kremb, S., Butterfoss, G.L., Xie, X., Fahs, H., and Gunsalus, K.C. (2022). A Statistical Framework for High-Content Phenotypic Profiling Using Cellular Feature Distributions. Commun. Biol., 5.","DOI":"10.1038\/s42003-022-04343-3"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Di Credico, A., Weiss, A., Corsini, M., Gaggi, G., Ghinassi, B., Wilbertz, J.H., and Di Baldassarre, A. (2023). Machine Learning Identifies Phenotypic Profile Alterations of Human Dopaminergic Neurons Exposed to Bisphenols and Perfluoroalkyls. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-49364-y"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.chembiol.2021.02.015","article-title":"High-Content Phenotypic and Pathway Profiling to Advance Drug Discovery in Diseases of Unmet Need","volume":"28","author":"Hughes","year":"2021","journal-title":"Cell Chem. Biol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"e00028-21","DOI":"10.1128\/mSystems.00028-21","article-title":"High-Content Imaging to Phenotype Antimicrobial Effects on Individual Bacteria at Scale","volume":"6","author":"Sridhar","year":"2021","journal-title":"mSystems"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Pinto-Coelho, L. (2023). How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications. Bioengineering, 10.","DOI":"10.20944\/preprints202311.1366.v1"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.ejmp.2021.02.007","article-title":"Data Preparation for Artificial Intelligence in Medical Imaging: A Comprehensive Guide to Open-Access Platforms and Tools","volume":"83","author":"Diaz","year":"2021","journal-title":"Phys. Medica"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.ejmp.2021.10.005","article-title":"Enhancing the Impact of Artificial Intelligence in Medicine: A Joint AIFM-INFN Italian Initiative for a Dedicated Cloud-Based Computing Infrastructure","volume":"91","author":"Retico","year":"2021","journal-title":"Phys. Medica"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Simon, B.D., Ozyoruk, K.B., Gelikman, D.G., Harmon, S.A., and T\u00fcrkbey, B. (2024). The Future of Multimodal Artificial Intelligence Models for Integrating Imaging and Clinical Metadata: A Narrative Review. Dir.","DOI":"10.4274\/dir.2024.242631"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"101945","DOI":"10.1016\/j.inffus.2023.101945","article-title":"Computational Approaches to Explainable Artificial Intelligence: Advances in Theory, Applications and Trends","volume":"100","author":"Arco","year":"2023","journal-title":"Inf. Fusion."},{"key":"ref_64","first-page":"3","article-title":"Supervised Machine Learning: A Review of Classification Techniques","volume":"160","author":"Kotsiantis","year":"2007","journal-title":"Emerg. Artif. Intell. Appl. Comput. Eng."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.dsm.2021.12.002","article-title":"Machine Learning-Based Approach: Global Trends, Research Directions, and Regulatory Standpoints","volume":"4","author":"Pugliese","year":"2021","journal-title":"Data Sci. Manag."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1134\/S1054661818010054","article-title":"Normal and Abnormal Tissue Classification in Positron Emission Tomography Oncological Studies","volume":"28","author":"Comelli","year":"2018","journal-title":"Pattern Recognit. Image Anal."},{"key":"ref_67","unstructured":"Zheng, Y., Williams, B.M., and Chen, K. (2020). Tissue Classification to Support Local Active Delineation of Brain Tumors. Medical Image Understanding and Analysis, Springer International Publishing."},{"key":"ref_68","unstructured":"Holl, A., Chen, J., and Guan, G. (2022, January 14\u201316). Unsupervised Learning Algorithms in Big Data: An Overview. Proceedings of the 2022 5th International Conference on Humanities Education and Social Sciences (ICHESS 2022), Chongqing, China."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1002\/ima.22168","article-title":"Unsupervised Tissue Classification of Brain MR Images for Voxel-Based Morphometry Analysis","volume":"26","author":"Agnello","year":"2016","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"120495","DOI":"10.1016\/j.eswa.2023.120495","article-title":"Reinforcement Learning Algorithms: A Brief Survey","volume":"231","author":"Shakya","year":"2023","journal-title":"Expert. Syst. Appl."},{"key":"ref_71","unstructured":"Li, Y. (2017). Deep Reinforcement Learning: An Overview. arXiv."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Hu, W., Zhang, Y., and Li, L. (2019). Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images. Sensors, 19.","DOI":"10.3390\/s19163584"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1067\/j.cpradiol.2021.06.006","article-title":"Deep Learning Network for Segmentation of the Prostate Gland with Median Lobe Enlargement in T2-Weighted MR Images: Comparison with Manual Segmentation Method","volume":"51","author":"Salvaggio","year":"2022","journal-title":"Curr. Probl. Diagn. Radiol."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Corso, R., Comelli, A., Salvaggio, G., and Tegolo, D. (2024). New Parametric 2D Curves for Modeling Prostate Shape in Magnetic Resonance Images. Symmetry, 16.","DOI":"10.3390\/sym16060755"},{"key":"ref_75","first-page":"239","article-title":"Recurrent Neural Networks-Architectures and Applications: Analyzing Architectures and Applications of Recurrent Neural Networks (RNNs) for Modeling Sequential Data and Time-Series Prediction","volume":"3","author":"Oluwafemi","year":"2023","journal-title":"Aust. J. Mach. Learn. Res. Appl."},{"key":"ref_76","unstructured":"Koller, D., and Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques, MIT press."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Tharwat, A., and Schenck, W. (2023). A Survey on Active Learning: State-of-the-Art, Practical Challenges and Research Directions. Mathematics, 11.","DOI":"10.3390\/math11040820"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Rainio, O., Teuho, J., and Kl\u00e9n, R. (2024). Evaluation Metrics and Statistical Tests for Machine Learning. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-56706-x"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Bertels, J., Eelbode, T., Berman, M., Vandermeulen, D., Maes, F., Bisschops, R., and Blaschko, M. (2019, January 13\u201317). Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory and Practice. Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China.","DOI":"10.1007\/978-3-030-32245-8_11"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Seoni, S., Shahini, A., Meiburger, K.M., Marzola, F., Rotunno, G., Acharya, U.R., Molinari, F., and Salvi, M. (2024). All You Need Is Data Preparation: A Systematic Review of Image Harmonization Techniques in Multi-Center\/Device Studies for Medical Support Systems. Comput. Methods Programs Biomed., 250.","DOI":"10.1016\/j.cmpb.2024.108200"},{"key":"ref_81","first-page":"124","article-title":"Artificial neual network: An overview","volume":"2023","author":"Qamar","year":"2023","journal-title":"Mesopotamian J. Comput. Sci."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Kacprzyk, J., and Pedrycz, W. (2015). Artificial Neural Network Models. Springer Handbook of Computational Intelligence, Springer.","DOI":"10.1007\/978-3-662-43505-2"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"e444","DOI":"10.7717\/peerj-cs.444","article-title":"Neural Network Hyperparameter Optimization for Prediction of Real Estate Prices in Helsinki","volume":"7","author":"Kalliola","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Magboo, V.P.C., and Abu, P.A.R. (2023). Analysis of Batch Size in the Assessment of Bone Metastasis from Bone Scans in Various Convolutional Neural Networks., Springer.","DOI":"10.1007\/978-981-99-3068-5_20"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Raximov, N., Kuvandikov, J., and Dilmurod, K. (2022, January 28\u201330). The Importance of Loss Function in Artificial Intelligence. Proceedings of the 2022 International Conference on Information Science and Communications Technologies (ICISCT), Tashkent, Uzbekistan.","DOI":"10.1109\/ICISCT55600.2022.10146883"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"5393","DOI":"10.30534\/ijatcse\/2020\/175942020","article-title":"Binary Cross Entropy with Deep Learning Technique for Image Classification","volume":"9","author":"Ruby","year":"2020","journal-title":"IJATCSE"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"16591","DOI":"10.1007\/s11042-022-13820-0","article-title":"The Effect of Choosing Optimizer Algorithms to Improve Computer Vision Tasks: A Comparative Study","volume":"82","author":"Hassan","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_88","first-page":"189","article-title":"Introduction to Artificial Neural Network","volume":"2","author":"Dongare","year":"2012","journal-title":"Int. J. Eng. Innov. Technol. (IJEIT)"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1007\/s10462-023-10662-6","article-title":"Autoencoders and Their Applications in Machine Learning: A Survey","volume":"57","author":"Berahmand","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"13521","DOI":"10.1007\/s10462-023-10466-8","article-title":"Deep Learning Modelling Techniques: Current Progress, Applications, Advantages, and Challenges","volume":"56","author":"Ahmed","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Lo Casto, A., Spartivento, G., Benfante, V., Di Raimondo, R., Ali, M., Di Raimondo, D., Tuttolomondo, A., Stefano, A., Yezzi, A., and Comelli, A. (2023). Artificial Intelligence for Classifying the Relationship between Impacted Third Molar and Mandibular Canal on Panoramic Radiographs. Life, 13.","DOI":"10.3390\/life13071441"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Krichen, M. (2023). Convolutional Neural Networks: A Survey. Computers, 12.","DOI":"10.3390\/computers12080151"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","article-title":"Convolutional Neural Networks: An Overview and Application in Radiology","volume":"9","author":"Yamashita","year":"2018","journal-title":"Insights Into Imaging"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.neucom.2019.10.008","article-title":"Impact of Fully Connected Layers on Performance of Convolutional Neural Networks for Image Classification","volume":"378","author":"Basha","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_95","unstructured":"Pinaya, W.H.L., Vieira, S., Garcia-Dias, R., and Mechelli, A. (2020). Convolutional Neural Networks. Machine learning, Elsevier."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Zafar, A., Aamir, M., Mohd Nawi, N., Arshad, A., Riaz, S., Alruban, A., Dutta, A.K., and Almotairi, S. (2022). A Comparison of Pooling Methods for Convolutional Neural Networks. Appl. Sci., 12.","DOI":"10.3390\/app12178643"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.tifs.2021.04.042","article-title":"Efficient Extraction of Deep Image Features Using Convolutional Neural Network (CNN) for Applications in Detecting and Analysing Complex Food Matrices","volume":"113","author":"Liu","year":"2021","journal-title":"Trends Food Sci. Technol."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.jormas.2019.06.002","article-title":"Deep Learning in Medical Image Analysis: A Third Eye for Doctors","volume":"120","author":"Fourcade","year":"2019","journal-title":"J. Stomatol. Oral Maxillofac. Surg."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Singha, A., Thakur, R.S., and Patel, T. (2021). Deep Learning Applications in Medical Image Analysis. Biomedical Data Mining for Information Retrieval: Methodologies, Techniques and Applications, John Wiley & Sons, Inc.","DOI":"10.1002\/9781119711278.ch11"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1080\/10408363.2018.1536111","article-title":"Deep Learning for Image Analysis: Personalizing Medicine Closer to the Point of Care","volume":"56","author":"Xie","year":"2019","journal-title":"Crit. Rev. Clin. Lab. Sci."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Razzak, M.I., Naz, S., and Zaib, A. (2018). Deep Learning for Medical Image Processing: Overview, Challenges and the Future. Classification in BioApps: Automation of Decision Making, IGI Global.","DOI":"10.1007\/978-3-319-65981-7_12"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Parthiban, S., Vijeesh, T., Gayathri, T., Shanmugaraj, B., Sharma, A., and Sathishkumar, R. (2023). Artificial Intelligence-Driven Systems Engineering for next-Generation Plant-Derived Biopharmaceuticals. Front. Plant Sci., 14.","DOI":"10.3389\/fpls.2023.1252166"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of Deep Learning: Concepts, CNN Architectures, Challenges, Applications, Future Directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.jare.2021.03.015","article-title":"A Review on Modern Defect Detection Models Using DCNNs\u2013Deep Convolutional Neural Networks","volume":"35","author":"Tulbure","year":"2022","journal-title":"J. Adv. Res."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Ketkar, N., Moolayil, J., Ketkar, N., and Moolayil, J. (2021). Convolutional Neural Networks. Deep Learning with Python: Learn Best Practices of Deep Learning Models with PyTorch, Apress.","DOI":"10.1007\/978-1-4842-5364-9"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Ragab, D.A., Attallah, O., Sharkas, M., Ren, J., and Marshall, S. (2021). A Framework for Breast Cancer Classification Using Multi-DCNNs. Comput. Biol. Med., 131.","DOI":"10.1016\/j.compbiomed.2021.104245"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"122983","DOI":"10.1016\/j.eswa.2023.122983","article-title":"Paradigm Shift from Artificial Neural Networks (ANNs) to Deep Convolutional Neural Networks (DCNNs) in the Field of Medical Image Processing","volume":"244","author":"Abut","year":"2023","journal-title":"Expert. Syst. Appl."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Piras, A., Corso, R., Benfante, V., Ali, M., Laudicella, R., Alongi, P., D\u2019Aviero, A., Cusumano, D., Boldrini, L., and Salvaggio, G. (2024). Artificial Intelligence and Statistical Models for the Prediction of Radiotherapy Toxicity in Prostate Cancer: A Systematic Review. Appl. Sci., 14.","DOI":"10.3390\/app142310947"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Vera-Rodriguez, R., Blazquez, M., Morales, A., Gonzalez-Sosa, E., Neves, J.C., and Proen\u00e7a, H. (2019, January 15\u201320). Facegenderid: Exploiting Gender Information in Dcnns Face Recognition Systems. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00278"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Liu, C., Ding, W., Xia, X., Zhang, B., Gu, J., Liu, J., Ji, R., and Doermann, D. (2019, January 15\u201320). Circulant Binary Convolutional Networks: Enhancing the Performance of 1-Bit Dcnns with Circulant Back Propagation. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00280"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1145\/3093336.3037746","article-title":"Sc-Dcnn: Highly-Scalable Deep Convolutional Neural Network Using Stochastic Computing","volume":"52","author":"Ren","year":"2017","journal-title":"ACM Sigplan Not."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.neucom.2019.10.007","article-title":"Autonomous Deep Learning: A Genetic DCNN Designer for Image Classification","volume":"379","author":"Ma","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.isprsjprs.2020.01.023","article-title":"Aerial Image Semantic Segmentation Using DCNN Predicted Distance Maps","volume":"161","author":"Chai","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"109573","DOI":"10.1016\/j.engappai.2024.109573","article-title":"An End-to-End Deep Convolutional Neural Network-Based Data-Driven Fusion Framework for Identification of Human Induced Pluripotent Stem Cell-Derived Endothelial Cells in Photomicrographs","volume":"139","author":"Iqbal","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"173","DOI":"10.3390\/biomedinformatics4010012","article-title":"Digital Pathology: A Comprehensive Review of Open-Source Histological Segmentation Software","volume":"4","author":"Pavone","year":"2024","journal-title":"BioMedInformatics"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"100348","DOI":"10.1016\/j.crmeth.2022.100348","article-title":"Spatial Statistics Is a Comprehensive Tool for Quantifying Cell Neighbor Relationships and Biological Processes via Tissue Image Analysis","volume":"2","author":"Summers","year":"2022","journal-title":"Cell Rep. Methods"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1038\/s41378-023-00562-8","article-title":"Computer Vision Meets Microfluidics: A Label-Free Method for High-Throughput Cell Analysis","volume":"9","author":"Zhou","year":"2023","journal-title":"Microsyst. Nanoeng."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"3953","DOI":"10.1016\/j.cell.2024.05.055","article-title":"Open-ST: High-Resolution Spatial Transcriptomics in 3D","volume":"187","author":"Schott","year":"2024","journal-title":"Cell"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1038\/s43586-022-00174-y","article-title":"Organoids","volume":"2","author":"Zhao","year":"2022","journal-title":"Nat. Rev. Methods Primers"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1038\/s41587-021-01044-w","article-title":"Cell Segmentation in Imaging-Based Spatial Transcriptomics","volume":"40","author":"Petukhov","year":"2021","journal-title":"Nat. Biotechnol."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yu, X., Yang, Y., Zhang, X., Zhang, Y., Zhang, L., Feng, R., and Xue, J. (2024). A Multi-Branched Semantic Segmentation Network Based on Twisted Information Sharing Pattern for Medical Images. Comput. Methods Programs Biomed., 243.","DOI":"10.1016\/j.cmpb.2023.107914"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"101504","DOI":"10.1016\/j.imu.2024.101504","article-title":"Deep Learning for Medical Image Segmentation: State-of-the-Art Advancements and Challenges","volume":"47","author":"Rayed","year":"2024","journal-title":"Inform. Med. Unlocked"},{"key":"ref_124","first-page":"1","article-title":"U-Net-Based Medical Image Segmentation","volume":"2022","author":"Yin","year":"2022","journal-title":"J. Healthc. Eng."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-030-33128-3_1","article-title":"Deep Learning in Medical Image Analysis","volume":"Volume 1213","author":"Lee","year":"2020","journal-title":"Deep Learning in Medical Image Analysis"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Taye, M.M. (2023). Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions. Computation, 11.","DOI":"10.3390\/computation11030052"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Giacopelli, G., Migliore, M., and Tegolo, D. (2023). NeuronAlg: An Innovative Neuronal Computational Model for Immunofluorescence Image Segmentation. Sensors, 23.","DOI":"10.2139\/ssrn.4333621"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1515\/cdbme-2023-1084","article-title":"Mask R-CNN Outperforms U-Net in Instance Segmentation for Overlapping Cells","volume":"9","author":"Rettenberger","year":"2023","journal-title":"Curr. Dir. Biomed. Eng."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"107208","DOI":"10.1016\/j.compag.2022.107208","article-title":"Generative Adversarial Networks (GANs) for Image Augmentation in Agriculture: A Systematic Review","volume":"200","author":"Lu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_131","first-page":"2672","article-title":"Generative Adversarial Nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Ahmad, Z., Jaffri, Z.U.A., Chen, M., and Bao, S. (2024). Understanding GANs: Fundamentals, Variants, Training Challenges, Applications, and Open Problems. Multimed. Tools Appl., 1\u201377.","DOI":"10.1007\/s11042-024-19361-y"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"107881","DOI":"10.1016\/j.engappai.2024.107881","article-title":"A Comprehensive Review of Synthetic Data Generation in Smart Farming by Using Variational Autoencoder and Generative Adversarial Network","volume":"131","author":"Akkem","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"101938","DOI":"10.1016\/j.artmed.2020.101938","article-title":"GANs for Medical Image Analysis","volume":"109","author":"Kazeminia","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_135","unstructured":"Rana, P. (2023). Analysis of Cellular and Subcellular Morphology Using Machine Learning in Microscopy Images. [Ph.D. Thesis, UNSW Sydney]."},{"key":"ref_136","unstructured":"Merchant, F., and Castleman, K. (2022). Microscope Image Processing, Academic Press."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1038\/s43586-021-00038-x","article-title":"Single-Molecule Localization Microscopy","volume":"1","author":"Lelek","year":"2021","journal-title":"Nat. Rev. Methods Primers"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1038\/s41592-021-01155-x","article-title":"Three-Dimensional Residual Channel Attention Networks Denoise and Sharpen Fluorescence Microscopy Image Volumes","volume":"18","author":"Chen","year":"2021","journal-title":"Nat. Methods"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1038\/s41592-021-01156-w","article-title":"Best Practices and Tools for Reporting Reproducible Fluorescence Microscopy Methods","volume":"18","author":"Senft","year":"2021","journal-title":"Nat. Methods"},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Relucenti, M., Familiari, G., Donfrancesco, O., Taurino, M., Li, X., Chen, R., Artini, M., Papa, R., and Selan, L. (2021). Microscopy Methods for Biofilm Imaging: Focus on SEM and VP-SEM Pros and Cons. Biology, 10.","DOI":"10.3390\/biology10010051"},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Wang, X., Sun, L., Chehri, A., and Song, Y. (2023). A Review of GAN-Based Super-Resolution Reconstruction for Optical Remote Sensing Images. Remote Sens., 15.","DOI":"10.3390\/rs15205062"},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Garcea, F., Serra, A., Lamberti, F., and Morra, L. (2023). Data Augmentation for Medical Imaging: A Systematic Literature Review. Comput. Biol. Med., 152.","DOI":"10.1016\/j.compbiomed.2022.106391"},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., and Guan, Q. (2022). Generative Adversarial Networks in Medical Image Augmentation: A Review. Comput. Biol. Med., 144.","DOI":"10.1016\/j.compbiomed.2022.105382"},{"key":"ref_144","unstructured":"Bos, J. (2021). Conditional Generative Deep Learning Models to Predict Fluorescence Microscopy Images from Transmitted Light Images. [Ph.D. Thesis, Tilburg University]."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"121449","DOI":"10.1109\/ACCESS.2024.3453664","article-title":"Deep Learning and Computer Vision Techniques for Enhanced Quality Control in Manufacturing Processes","volume":"12","author":"Islam","year":"2024","journal-title":"IEEE Access"},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Wang, R., Butt, D., Cross, S., Verkade, P., and Achim, A. (2023). Bright-Field to Fluorescence Microscopy Image Translation for Cell Nuclei Health Quantification. Biol. Imaging, 3.","DOI":"10.1017\/S2633903X23000120"},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Breznik, E., Wetzer, E., Lindblad, J., and Sladoje, N. (2024). Cross-Modality Sub-Image Retrieval Using Contrastive Multimodal Image Representations. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-68800-1"},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Nikolenko, S.I. (2021). Synthetic Data for Deep Learning. Springer Optimization and Its Applications, Springer International Publishing.","DOI":"10.1007\/978-3-030-75178-4"},{"key":"ref_149","first-page":"6343","article-title":"Coupled Real-Synthetic Domain Adaptation for Real-World Deep Depth Enhancement","volume":"29","author":"Gu","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Gao, W., Wang, C., Li, Q., Zhang, X., Yuan, J., Li, D., Sun, Y., Chen, Z., and Gu, Z. (2022). Application of Medical Imaging Methods and Artificial Intelligence in Tissue Engineering and Organ-on-a-Chip. Front. Bioeng. Biotechnol., 10.","DOI":"10.3389\/fbioe.2022.985692"},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Jiang, X., Hu, Z., Wang, S., and Zhang, Y. (2023). Deep Learning for Medical Image-Based Cancer Diagnosis. Cancers, 15.","DOI":"10.3390\/cancers15143608"},{"key":"ref_152","first-page":"857","article-title":"Revolutionizing Tumor Detection and Classification in Multimodality Imaging Based on Deep Learning Approaches: Methods, Applications and Limitations","volume":"32","author":"Hussain","year":"2024","journal-title":"J. X-Ray Sci. Technol."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"064001","DOI":"10.1117\/1.AP.6.6.064001","article-title":"Cross-Modality Transformations in Biological Microscopy Enabled by Deep Learning","volume":"6","author":"Hassan","year":"2024","journal-title":"Adv. Photonics"},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Nazir, A., Hussain, A., Singh, M., and Assad, A. (2025). A Novel Approach in Cancer Diagnosis: Integrating Holography Microscopic Medical Imaging and Deep Learning Techniques\u2013Challenges and Future Trends. Biomed. Phys. Eng. Express, 11.","DOI":"10.1088\/2057-1976\/ad9eb7"},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"e38","DOI":"10.1002\/mef2.38","article-title":"Recent Advances of Transformers in Medical Image Analysis: A Comprehensive Review","volume":"2","author":"Xia","year":"2023","journal-title":"MedComm\u2013Future Med."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"106126","DOI":"10.1016\/j.engappai.2023.106126","article-title":"Vision Transformers in Medical Computer Vision\u2014A Contemplative Retrospection","volume":"122","author":"Parvaiz","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"102802","DOI":"10.1016\/j.media.2023.102802","article-title":"Transformers in Medical Imaging: A Survey","volume":"88","author":"Shamshad","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_158","unstructured":"Dosovitskiy, A. (2020). An Image Is Worth 16 \u00d7 16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_159","unstructured":"Pereira, G.A., and Hussain, M. (2024). A Review of Transformer-Based Models for Computer Vision Tasks: Capturing Global Context and Spatial Relationships. arXiv."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3505244","article-title":"Transformers in Vision: A Survey","volume":"54","author":"Khan","year":"2022","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.imed.2022.07.002","article-title":"Transformers in Medical Image Analysis","volume":"3","author":"He","year":"2023","journal-title":"Intell. Med."},{"key":"ref_162","doi-asserted-by":"crossref","unstructured":"Alif, M.A., Hussain, M., Tucker, G., and Iwnicki, S. (2024). BoltVision: A Comparative Analysis of CNN, CCT, and ViT in Achieving High Accuracy for Missing Bolt Classification in Train Components. Machines, 12.","DOI":"10.3390\/machines12020093"},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1038\/s41587-021-01182-1","article-title":"Spatial Components of Molecular Tissue Biology","volume":"40","author":"Palla","year":"2022","journal-title":"Nat. Biotechnol."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"102920","DOI":"10.1016\/j.media.2023.102920","article-title":"Segmentation in Large-Scale Cellular Electron Microscopy with Deep Learning: A Literature Survey","volume":"89","author":"Aswath","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_165","unstructured":"Vadori, V., Gra\u00efc, J.-M., Peruffo, A., Vadori, G., Finos, L., and Grisan, E. (2024). CISCA and CytoDArk0: A Cell Instance Segmentation and Classification Method for Histo(Patho)Logical Image Analyses and a New, Open, Nissl-Stained Dataset for Brain Cytoarchitecture Studies. arXiv."},{"key":"ref_166","doi-asserted-by":"crossref","unstructured":"Pu, Q., Xi, Z., Yin, S., Zhao, Z., and Zhao, L. (2024). Advantages of Transformer and Its Application for Medical Image Segmentation: A Survey. BioMedical Eng. OnLine, 23.","DOI":"10.1186\/s12938-024-01212-4"},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"102762","DOI":"10.1016\/j.media.2023.102762","article-title":"Transforming Medical Imaging with Transformers? A Comparative Review of Key Properties, Current Progresses, and Future Perspectives","volume":"85","author":"Li","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_168","doi-asserted-by":"crossref","unstructured":"Choi, S.R., and Lee, M. (2023). Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review. Biology, 12.","DOI":"10.3390\/biology12071033"},{"key":"ref_169","doi-asserted-by":"crossref","unstructured":"Bi, L., Buehner, U., Fu, X., Williamson, T., Choong, P., and Kim, J. (2024). Hybrid CNN-Transformer Network for Interactive Learning of Challenging Musculoskeletal Images. Comput. Methods Programs Biomed., 243.","DOI":"10.1016\/j.cmpb.2023.107875"},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"1676","DOI":"10.3390\/tomography10100123","article-title":"A Hybrid CNN-Transformer Model for Predicting N Staging and Survival in Non-Small Cell Lung Cancer Patients Based on CT-Scan","volume":"10","author":"Wang","year":"2024","journal-title":"Tomography"},{"key":"ref_171","doi-asserted-by":"crossref","unstructured":"Jiang, M., Zhu, Y., and Zhang, X. (2024). CoVi-Net: A Hybrid Convolutional and Vision Transformer Neural Network for Retinal Vessel Segmentation. Comput. Biol. Med., 170.","DOI":"10.1016\/j.compbiomed.2024.108047"},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"189477","DOI":"10.1109\/ACCESS.2024.3516535","article-title":"Hybrid CNN-Transformer Architecture with Xception-Based Feature Enhancement for Accurate Breast Cancer Classification","volume":"12","author":"Zeynali","year":"2024","journal-title":"IEEE Access"},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"2917","DOI":"10.1007\/s10462-023-10595-0","article-title":"A Survey of the Vision Transformers and Their CNN-Transformer Based Variants","volume":"56","author":"Khan","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.neunet.2023.11.039","article-title":"A Versatile Wavelet-Enhanced CNN-Transformer for Improved Fluorescence Microscopy Image Restoration","volume":"170","author":"Wang","year":"2024","journal-title":"Neural Netw."},{"key":"ref_175","unstructured":"Dutta, D., Chetia, D., Sonowal, N., and Kalita, S.K. (2025). State-of-the-Art Transformer Models for Image Super-Resolution: Techniques, Challenges, and Applications. arXiv."},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"129000","DOI":"10.1109\/ACCESS.2024.3457823","article-title":"Multi-TranResUnet: An Improved Transformer Network for Solving Multi-Scale Issues in Image Segmentation","volume":"12","author":"Kang","year":"2024","journal-title":"IEEE Access"},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1038\/s41420-023-01559-y","article-title":"CellDeathPred: A Deep Learning Framework for Ferroptosis and Apoptosis Prediction Based on Cell Painting","volume":"9","author":"Schorpp","year":"2023","journal-title":"Cell Death Discov."},{"key":"ref_178","doi-asserted-by":"crossref","unstructured":"Pattarone, G., Acion, L., Simian, M., Mertelsmann, R., Follo, M., and Iarussi, E. (2021). Learning Deep Features for Dead and Living Breast Cancer Cell Classification without Staining. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-89895-w"},{"key":"ref_179","doi-asserted-by":"crossref","unstructured":"Van Valen, D.A., Kudo, T., Lane, K.M., Macklin, D.N., Quach, N.T., DeFelice, M.M., Maayan, I., Tanouchi, Y., Ashley, E.A., and Covert, M.W. (2016). Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments. PLOS Comput. Biol., 12.","DOI":"10.1371\/journal.pcbi.1005177"},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1200\/JCO.2020.38.6_suppl.279","article-title":"Artificial Intelligence for Streamlined Immunofluorescence-Based Biomarker Discovery in Prostate Cancer","volume":"38","author":"Nguyen","year":"2020","journal-title":"J. Clin. Oncol."},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1364\/BOE.10.001044","article-title":"High-Throughput, High-Resolution Deep Learning Microscopy Based on Registration-Free Generative Adversarial Network","volume":"10","author":"Zhang","year":"2019","journal-title":"Biomed. Opt. Express"},{"key":"ref_182","first-page":"476","article-title":"Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy","volume":"Volume 12908","author":"Cattin","year":"2021","journal-title":"Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2021"},{"key":"ref_183","doi-asserted-by":"crossref","unstructured":"Ali, R., Balamurali, M., and Varamini, P. (2022). Deep Learning-Based Artificial Intelligence to Investigate Targeted Nanoparticles\u2019 Uptake in TNBC Cells. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms232416070"},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"21420","DOI":"10.1109\/ACCESS.2019.2896920","article-title":"RIC-Unet: An Improved Neural Network Based on Unet for Nuclei Segmentation in Histology Images","volume":"7","author":"Zeng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_185","doi-asserted-by":"crossref","unstructured":"Park, S., Veluvolu, V., Martin, W.S., Nguyen, T., Park, J., Sackett, D.L., Boccara, C., and Gandjbakhche, A. (2022). Label-Free, Non-Invasive, and Repeatable Cell Viability Bioassay Using Dynamic Full-Field Optical Coherence Microscopy and Supervised Machine Learning. Biomed. Opt. Express, 13.","DOI":"10.1364\/BOE.452471"},{"key":"ref_186","doi-asserted-by":"crossref","first-page":"026110","DOI":"10.1063\/5.0141730","article-title":"Label Free Identification of Different Cancer Cells Using Deep Learning-Based Image Analysis","volume":"1","author":"Gardner","year":"2023","journal-title":"APL Mach. Learn."},{"key":"ref_187","doi-asserted-by":"crossref","unstructured":"Lavitt, F., Rijlaarsdam, D.J., van der Linden, D., Weglarz-Tomczak, E., and Tomczak, J.M. (2021). Deep Learning and Transfer Learning for Automatic Cell Counting in Microscope Images of Human Cancer Cell Lines. Appl. Sci., 11.","DOI":"10.3390\/app11114912"},{"key":"ref_188","doi-asserted-by":"crossref","unstructured":"Oei, R.W., Hou, G., Liu, F., Zhong, J., Zhang, J., An, Z., Xu, L., and Yang, Y. (2019). Convolutional Neural Network for Cell Classification Using Microscope Images of Intracellular Actin Networks. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0213626"},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1186\/s13000-020-01003-0","article-title":"Deep Learning-Based Image Analysis Methods for Brightfield-Acquired Multiplex Immunohistochemistry Images","volume":"15","author":"Fassler","year":"2020","journal-title":"Diagn. Pathol."},{"key":"ref_190","first-page":"203143","article-title":"CellViT: Vision Transformers for Precise Cell Segmentation and Classification","volume":"94","author":"Rempe","year":"2024","journal-title":"Med. Image Anal."},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1038\/s41698-024-00577-y","article-title":"Artificial Intelligence-Based Assessment of PD-L1 Expression in Diffuse Large B Cell Lymphoma","volume":"8","author":"Yan","year":"2024","journal-title":"NPJ Precis. Oncol."},{"key":"ref_192","doi-asserted-by":"crossref","unstructured":"Sarker, M.M., Makhlouf, Y., Craig, S.G., Humphries, M.P., Loughrey, M., James, J.A., Salto-Tellez, M., O\u2019Reilly, P., and Maxwell, P. (2021). A Means of Assessing Deep Learning-Based Detection of ICOS Protein Expression in Colon Cancer. Cancers, 13.","DOI":"10.3390\/cancers13153825"},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"100054","DOI":"10.1016\/j.modpat.2022.100054","article-title":"The Role of Artificial Intelligence in Accurate Interpretation of HER2 Immunohistochemical Scores 0 and 1+ in Breast Cancer","volume":"36","author":"Wu","year":"2023","journal-title":"Mod. Pathol."},{"key":"ref_194","doi-asserted-by":"crossref","unstructured":"Ferreira, E.K.G.D., and Silveira, G.F. (2024). Classification and Counting of Cells in Brightfield Microscopy Images: An Application of Convolutional Neural Networks. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-59625-z"},{"key":"ref_195","doi-asserted-by":"crossref","unstructured":"Rudigkeit, S., Reindl, J.B., Matejka, N., Ramson, R., Sammer, M., Dollinger, G., and Reindl, J. (2021). CeCILE\u2014An Artificial Intelligence Based Cell-Detection for the Evaluation of Radiation Effects in Eucaryotic Cells. Front. Oncol., 11.","DOI":"10.3389\/fonc.2021.688333"},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"eaba6156","DOI":"10.1126\/sciadv.aba6156","article-title":"Detection of Response to Tumor Microenvironment\u2013Targeted Cellular Immunotherapy Using Nano-Radiomics","volume":"6","author":"Devkota","year":"2020","journal-title":"Sci. Adv."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/2\/59\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:35:03Z","timestamp":1760027703000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/2\/59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,15]]},"references-count":196,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["jimaging11020059"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11020059","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,15]]}}}