{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T05:16:44Z","timestamp":1788239804605,"version":"build-2803163510"},"reference-count":59,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,30]],"date-time":"2026-08-30T00:00:00Z","timestamp":1788048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council","award":["114-2314-B-002-264"],"award-info":[{"award-number":["114-2314-B-002-264"]}]},{"award":["114-2314-B-002-264"],"award-info":[{"award-number":["114-2314-B-002-264"]}],"id":[{"id":"https:\/\/ror.org\/02kv4zf79","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Endobronchial ultrasound (EBUS) is widely used for the diagnosis of pulmonary lesions. When combined with rapid on-site cytologic evaluation (ROSE), it can improve diagnostic accuracy and facilitate early identification of malignancy origin. However, differentiating primary lung cancer from metastatic tumors (e.g., breast or colorectal origin) during ROSE remains challenging due to limited cytopathology support and morphological similarities between tumor cells. This study aimed to develop a computer-aided diagnostic (CAD) system for classifying malignancy types from limited cytological samples to facilitate clinical decision-making. Methods: We utilized a retrospective dataset of cytological images obtained during EBUS procedures between November 2018 and June 2024. The study focused on classifying three malignancies: pulmonary adenocarcinoma, metastatic breast cancer, and metastatic colorectal cancer. A deep learning-based model (PLFCH) was developed, integrating few-shot learning, parameter-efficient fine-tuning, and a hybrid CNN\u2013Transformer architecture to address limited annotated data. Results: A total of 41 patients with 346 cytological images were included. Under a 3-way 5-shot setting, the proposed model achieved an accuracy of 49.26%, outperforming existing few-shot learning methods, with precision, recall, and F1-score of 0.477, 0.493, and 0.480, respectively. Increasing the number of reference images to 20 per class further improved accuracy to 55.48%. Conclusions: The proposed framework demonstrated improved performance for cytological classification under limited data conditions. These findings provide an early proof of concept for applying few-shot learning to cytological assessment during EBUS procedures. Further improvements in model performance and validation in prospective, real-time clinical settings are required before its potential role in assisting diagnostic decision-making can be established.<\/jats:p>","DOI":"10.3390\/diagnostics16172790","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T04:24:03Z","timestamp":1788150243000},"page":"2790","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Few-Shot Learning for Cytological Classification of Primary Lung Cancer and Pulmonary Metastases During Endobronchial Ultrasound"],"prefix":"10.3390","volume":"16","author":[{"given":"Ching-Kai","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, College of Engineering, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan"},{"name":"Department of Medicine, National Taiwan University Cancer Center, Taipei 106, Taiwan"},{"name":"Department of Internal Medicine, National Taiwan University Hospital, Taipei 100, Taiwan"},{"name":"Department of Internal Medicine, National Taiwan University Hsin-Chu Hospital, Hsinchu 300, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Di-Chun","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, College of Engineering, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Hsin-Hung","family":"Chou","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, College of Engineering, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"I-Shiow","family":"Jan","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei 100, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Yun-Chien","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Taiwan University, Taipei 106, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1564","DOI":"10.1016\/j.cell.2023.03.003","article-title":"Metastasis","volume":"186","author":"Gerstberger","year":"2023","journal-title":"Cell"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2585","DOI":"10.21037\/jtd.2020.04.28","article-title":"The epidemiology and biology of pulmonary metastases","volume":"13","author":"Gerull","year":"2021","journal-title":"J. Thorac. Dis."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1378\/chest.126.1.122","article-title":"Real-time Endobronchial Ultrasound-Guided Transbronchial Needle Apiration of Mediastinal and Hilar Lymph Nodes","volume":"126","author":"Yasufuku","year":"2004","journal-title":"Chest"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1378\/chest.12-0350","article-title":"Complications, Consequences, and Practice Patterns of Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration: Results of the AQuIRE registry","volume":"143","author":"Eapen","year":"2013","journal-title":"Chest"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1002\/cncy.21319","article-title":"ROSEs (Rapid on-site evaluations) to our patients: The impact on laboratory resources and patient care","volume":"121","year":"2013","journal-title":"Cancer Cytopathol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.1016\/j.athoracsur.2012.09.074","article-title":"Rapid On-Site Cytologic Evaluation During Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration for Nodal Staging in Patients with Lung Cancer","volume":"95","author":"Nakajima","year":"2013","journal-title":"Ann. Thorac. Surg."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1093\/jjco\/hyw180","article-title":"Utility of rapid on-site cytologic evaluation during endobronchial ultrasound with a guide sheath for peripheral pulmonary lesions","volume":"47","author":"Izumo","year":"2017","journal-title":"Jpn. J. Clin. Oncol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1378\/chest.13-0756","article-title":"The Role of the Pulmonologist in Rapid On-site Cytologic Evaluation of Transbronchial Needle Aspiration","volume":"145","author":"Bonifazi","year":"2014","journal-title":"Chest"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1684","DOI":"10.1016\/j.jfma.2020.01.001","article-title":"Rapid on-site cytologic evaluation by pulmonologist improved diagnostic accuracy of endobronchial ultrasound-guided transbronchial biopsy","volume":"119","author":"Lin","year":"2020","journal-title":"J. Formos. Med. Assoc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"102691","DOI":"10.1016\/j.media.2022.102691","article-title":"Deep learning for computational cytology: A survey","volume":"84","author":"Jiang","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Thakur, N., Alam, M.R., Abdul-Ghafar, J., and Chong, Y. (2022). Recent Application of Artificial Intelligence in Non-Gynecological Cancer Cytopathology: A Systematic Review. Cancers, 14.","DOI":"10.3390\/cancers14143529"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e210014","DOI":"10.1148\/ryai.2021210014","article-title":"Training strategies for radiology deep learning models in data-limited scenarios","volume":"3","author":"Candemir","year":"2021","journal-title":"Radiol. Artif. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6","DOI":"10.21037\/vats-2020-lm-01","article-title":"Is preoperative histological diagnosis in lung metastases necessary?","volume":"6","author":"Subotic","year":"2021","journal-title":"Video-Assist. Thorac. Surg."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1109\/TCBB.2023.3282226","article-title":"Medical Tumor Image Classification Based on Few-Shot Learning","volume":"21","author":"Wang","year":"2024","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mohanta, A., Dey Roy, S., Nath, N., and Bhowmik, M.K. (2023, January 12\u201315). Domain Adapted Few-Shot Learning for Breast Histopathological Image Classification. Proceedings of the International Conference on Pattern Recognition and Machine Intelligence (PReMI), Kolkata, India.","DOI":"10.1007\/978-3-031-45170-6_42"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xu, B., Wang, Q., Li, L., Yang, J., Lin, H., and Ding, F. (2023, January 5\u20138). A novel few-shot learning approach for the classification of histological images of liver tumors. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Istanbul, Turkey.","DOI":"10.1109\/BIBM58861.2023.10385787"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hu, S.X., Li, D., St\u00fchmer, J., Kim, M., and Hospedales, T.M. (2022, January 19\u201324). Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00886"},{"key":"ref_18","unstructured":"Fifty, C., Duan, D., Junkins, R.G., Amid, E., Leskovec, J., R\u00e9, C., and Thrun, S. (2024, January 7\u201311). Context-Aware Meta-Learning. Proceedings of the International Conference on Learning Representations (ICLR), Vienna, Austria."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3469","DOI":"10.1109\/TMM.2023.3311646","article-title":"SgVA-CLIP: Semantic-Guided Visual Adapting of Vision-Language Models for Few-Shot Image Classification","volume":"26","author":"Peng","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"ref_20","unstructured":"Singh, A., and Jamali-Rad, H. (2022). Transductive Decoupled Variational Inference for Few-Shot Classification. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhou, F., Wang, P., Zhang, L., Wei, W., and Zhang, Y. (2023, January 17\u201324). Revisiting Prototypical Network for Cross Domain Few-Shot Learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01921"},{"key":"ref_22","unstructured":"Baharoon, M., Qureshi, W., Ouyang, J., Xu, Y., Aljouie, A., and Peng, W. (2023). Evaluating General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Huix, J.P., Ganeshan, A.R., Haslum, J.F., S\u00f6derberg, M., Matsoukas, C., and Smith, K. (2024, January 4\u20138). Are Natural Domain Foundation Models Useful for Medical Image Classification?. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV57701.2024.00746"},{"key":"ref_24","unstructured":"Dutt, R., Ericsson, L., Sanchez, P., Tsaftaris, S.A., and Hospedales, T. (2024, January 3\u20135). Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity. Proceedings of the 7th International Conference on Medical Imaging with Deep Learning (MIDL), Paris, France. Available online: https:\/\/proceedings.mlr.press\/v250\/dutt24a.html."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Basu, S., Hu, S., Massiceti, D., and Feizi, S. (2024, January 20\u201327). Strong Baselines for Parameter-Efficient Few-Shot Fine-Tuning. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), Vancouver, BC, Canada.","DOI":"10.1609\/aaai.v38i10.28978"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"104120","DOI":"10.1016\/j.jvcir.2024.104120","article-title":"Fine-grained image classification based on TinyVit object location and graph convolution network","volume":"100","author":"Zheng","year":"2024","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_27","unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., and Wierstra, D. (2016, January 5\u201310). Matching Networks for One Shot Learning. Proceedings of the 30th Conference on Neural Information Processing Systems (NeurIPS), Barcelona, Spain. Available online: https:\/\/proceedings.neurips.cc\/paper\/6385-matching-networks-for-one-shot-learning."},{"key":"ref_28","unstructured":"Snell, J., Swersky, K., and Zemel, R. (2017, January 4\u20139). Prototypical Networks for Few-shot Learning. Proceedings of the 31st Conference on Neural Information Processing Systems (NeurIPS), Long Beach, CA, USA. Available online: https:\/\/proceedings.neurips.cc\/paper\/6996-prototypical-networks-for-few-shot-learning."},{"key":"ref_29","unstructured":"Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W. (2022, January 25\u201329). LoRA: Low-Rank Adaptation of Large Language Models. Proceedings of the 10th International Conference on Learning Representations (ICLR), Virtual Event. Available online: https:\/\/openreview.net\/forum?id=nZeVKeeFYf9."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., and Zhang, L. (2021, January 11\u201317). CvT: Introducing Convolutions to Vision Transformers. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"ref_31","unstructured":"Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020, January 13\u201318). A Simple Framework for Contrastive Learning of Visual Representations. Proceedings of the 37th International Conference on Machine Learning (ICML), Virtual Event. Available online: https:\/\/proceedings.mlr.press\/v119\/chen20j.html."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_33","unstructured":"Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., and J\u00e9gou, H. (2021, January 18\u201324). Training data-efficient image transformers & distillation through attention. Proceedings of the 38th International Conference on Machine Learning (ICML), Virtual Event. Available online: https:\/\/proceedings.mlr.press\/v139\/touvron21a.html."},{"key":"ref_34","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021, January 3\u20137). An Image is Worth 16 \u00d7 16 Words: Transformers for Image Recognition at Scale. Proceedings of the 9th International Conference on Learning Representations (ICLR), Virtual Event. Available online: https:\/\/openreview.net\/forum?id=YicbFdNTTy."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lee, K., Maji, S., Ravichandran, A., and Soatto, S. (2019, January 16\u201320). Meta-Learning With Differentiable Convex Optimization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01091"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5009","DOI":"10.1109\/TCSVT.2023.3248791","article-title":"Dual Transformer With Multi-Grained Assembly for Fine-Grained Visual Classification","volume":"33","author":"Ji","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_37","unstructured":"Chou, P.Y., Kao, Y.Y., and Lin, C.H. (2023). Fine-grained Visual Classification with High-temperature Refinement and Background Suppression. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4529","DOI":"10.1109\/TIP.2024.3441813","article-title":"Multi-Granularity Part Sampling Attention for Fine-Grained Visual Classification","volume":"33","author":"Wang","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yu, W., Liu, Y., Zhao, Y., Huang, H., Liu, J., Yao, X., Li, J., Xie, Z., Jiang, L., and Wu, H. (2022). Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.821594"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1038\/s41591-024-02915-w","article-title":"Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning","volume":"30","author":"Tian","year":"2024","journal-title":"Nat. Med."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1038\/s41746-026-02359-1","article-title":"Deep learning for malignancy and tumor origin prediction using cytology or histopathology whole slide images","volume":"9","author":"Wang","year":"2026","journal-title":"npj Digit. Med."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1038\/s41746-026-02604-7","article-title":"Soft multiclass feature augmented deep learning to predict tumor origins using cytology or histology whole slide images","volume":"9","author":"Wang","year":"2026","journal-title":"npj Digit. Med."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Park, H.S., Chong, Y., Lee, Y., Yim, K., Seo, K.J., Hwang, G., Kim, D., Gong, G., Cho, N.H., and Yoo, C.W. (2023). Deep Learning-Based Computational Cytopathologic Diagnosis of Metastatic Breast Carcinoma in Pleural Fluid. Cells, 12.","DOI":"10.3390\/cells12141847"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"7789","DOI":"10.1109\/TCSVT.2023.3282777","article-title":"Few-Shot Learning Meets Transformer: Unified Query-Support Transformers for Few-Shot Classification","volume":"33","author":"Wang","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"107339","DOI":"10.1016\/j.neunet.2025.107339","article-title":"Enhancing few-shot image classification through learnable multi-scale embedding and attention mechanisms","volume":"187","author":"Askari","year":"2025","journal-title":"Neural Netw."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Cheng, H., Yang, S., Zhou, J.T., Guo, L., and Wen, B. (2023, January 2\u20136). Frequency Guidance Matters in Few-Shot Learning. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.01085"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.neunet.2023.05.037","article-title":"SCL: Self-supervised contrastive learning for few-shot image classification","volume":"165","author":"Lim","year":"2023","journal-title":"Neural Netw."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1038\/s41571-019-0252-y","article-title":"Artificial intelligence in digital pathology\u2014New tools for diagnosis and precision oncology","volume":"16","author":"Bera","year":"2019","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","article-title":"A guide to deep learning in healthcare","volume":"25","author":"Esteva","year":"2019","journal-title":"Nat. Med."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.jasc.2019.03.003","article-title":"Artificial intelligence in cytopathology: A review of the literature and overview of commercial landscape","volume":"8","author":"Landau","year":"2019","journal-title":"J. Am. Soc. Cytopathol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"9047","DOI":"10.1002\/cam4.4383","article-title":"Effectiveness of convolutional neural networks in the interpretation of pulmonary cytologic images in endobronchial ultrasound procedures","volume":"10","author":"Lin","year":"2021","journal-title":"Cancer Med."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"105474","DOI":"10.1016\/j.dib.2020.105474","article-title":"A dataset of microscopic peripheral blood cell images for development of automatic recognition systems","volume":"30","author":"Acevedo","year":"2020","journal-title":"Data Brief"},{"key":"ref_54","unstructured":"Borkowski, A.A., Bui, M.M., Thomas, L.B., Wilson, C.P., DeLand, L.A., and Mastorides, S.M. (2019). Lung and Colon Cancer Histopathological Image Dataset (LC25000). arXiv."},{"key":"ref_55","unstructured":"Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S. (2011). The Caltech UCSD Birds-200-2011(CUB-200-2011), California Institute of Technology. Available online: https:\/\/www.vision.caltech.edu\/datasets\/cub_200_2011\/."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., and Summers, R.M. (2017, January 21\u201326). ChestX-ray8: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.369"},{"key":"ref_57","unstructured":"Codella, N., Rotemberg, V., Tschandl, P., Celebi, M.E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., and Marchetti, M. (2019). Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC). arXiv."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1109\/JSTARS.2019.2918242","article-title":"EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification","volume":"12","author":"Helber","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Mohanty, S.P., Hughes, D.P., and Salath\u00e9, M. (2016). Using Deep Learning for Image-Based Plant Disease Detection. Front. Plant Sci., 7.","DOI":"10.3389\/fpls.2016.01419"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/17\/2790\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T04:24:59Z","timestamp":1788236699000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/17\/2790"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,30]]},"references-count":59,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["diagnostics16172790"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16172790","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,30]]}}}