Optimizing vitiligo diagnosis with ResNet and Swin transformer deep learning models: a study on performance and interpretability
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Vitiligo is a hypopigmented skin disease characterized by the loss of melanin. The progressive nature and widespread incidence of vitiligo necessitate timely and accurate detection. Usually, a single diagnostic test often falls short of providing definitive confirmation of the condition, necessitating the assessment by dermatologists who specialize in vitiligo. However, the current scarcity of such specialized medical professionals presents a significant challenge. To mitigate this issue and enhance diagnostic accuracy, it is essential to build deep learning models that can support and expedite the detection process. This study endeavors to establish a deep learning framework to enhance the diagnostic accuracy of vitiligo. To this end, a comparative analysis of five models including ResNet (ResNet34, ResNet50, and ResNet101 models) and Swin Transformer series (Swin Transformer Base, and Swin Transformer Large models), were conducted under the uniform condition to identify the model with superior classification capabilities. Moreover, the study sought to augment the interpretability of these models by selecting one that not only provides accurate diagnostic outcomes but also offers visual cues highlighting the regions pertinent to vitiligo. The empirical findings reveal that the Swin Transformer Large model achieved the best performance in classification, whose AUC, accuracy, sensitivity, and specificity are 0.94, 93.82%, 94.02%, and 93.5%, respectively. In terms of interpretability, the highlighted regions in the class activation map correspond to the lesion regions of the vitiligo images, which shows that it effectively indicates the specific category regions associated with the decision-making of dermatological diagnosis. Additionally, the visualization of feature maps generated in the middle layer of the deep learning model provides insights into the internal mechanisms of the model, which is valuable for improving the interpretability of the model, tuning performance, and enhancing clinical applicability. The outcomes of this study underscore the significant potential of deep learning models to revolutionize medical diagnosis by improving diagnostic accuracy and operational efficiency. The research highlights the necessity for ongoing exploration in this domain to fully leverage the capabilities of deep learning technologies in medical diagnostics.
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Swin Transformer deep learning models achieved better vitiligo classification performance than Resnet models on dermoscopy images (AUC = 0.94).
“Zhong et al (2024) compared Resnet and Swin Transformer deep learning models on dermoscopy images, with the latter model achieving better vitiligo classification performance (AUC = 0.94).”
From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Zhong et al, 2024
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Densely Connected Convolutional Networks
Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2016) · arXiv · doi:10.48550/arxiv.1608.06993 · Cited by 48,960 · see citing works
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566 · see citing works
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00986 · Cited by 35,534 · see citing works
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Tan, M., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1905.11946 · Cited by 17,072 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
Swin-Unet: Unet-like pure transformer for medical image segmentation
Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., & Wang, M. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-25066-8_9 · Cited by 5,640 · see citing works
Self-citationSwinIR: Image Restoration Using Swin Transformer
Liang, J., Cao, J., Sun, G., Zhang, K., Gool, L. V., & Timofte, R. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00210 · Cited by 5,150 · see citing works
Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., et al. (2018) · 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) · doi:10.1109/isbi.2018.8363547 · Cited by 2,620 · see citing works
Haenssle, H. A., Fink, C., Schneiderbauer, R., Toberer, F., Buhl, T., Blum, A., Kalloo, A., Hassen, A. B. H., Thomas, L., Enk, A., et al. (2018) · Annals of Oncology · doi:10.1093/annonc/mdy166 · Cited by 1,571 · see citing works
Deep Learning for Medical Image Processing: Overview, Challenges and the Future
Razzak, I., Naz, S., & Zaib, A. (2017) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-3-319-65981-7_12 · Cited by 1,305 · see citing works
Medical image segmentation using deep learning: A survey
Wang, R., Lei, T., Cui, R., Zhang, B., Meng, H., & Nandi, A. K. (2022) · IET Image Processing · doi:10.1049/ipr2.12419 · Cited by 947 · see citing works
Uddin, S., Haque, I., Lu, H., Moni, M. A., & Gide, E. (2022) · Scientific Reports · doi:10.1038/s41598-022-10358-x · Cited by 732 · see citing works
Han, S. S., Kim, M. S., Lim, W., Park, G., Park, I., & Chang, S. E. (2018) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2018.01.028 · Cited by 715 · see citing works
A methodological approach to the classification of dermoscopy images
Celebi, M. E., Kingravi, H. A., Uddin, B., Iyatomi, H., Aslandogan, Y. A., Stoecker, W. V., & Moss, R. H. (2007) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2007.01.003 · Cited by 697 · see citing works
Brinker, T. J., Hekler, A., Enk, A. H., Klode, J., Hauschild, A., Berking, C., Schilling, B., Haferkamp, S., Schadendorf, D., Holland-Letz, T., et al. (2019) · European Journal of Cancer · doi:10.1016/j.ejca.2019.04.001 · Cited by 605 · cited 1× · see citing works
“Furthermore, several studies have demonstrated the remarkable diagnostic and classification abilities of deep learning in tasks related to melanoma image analysis 37–39 .”
BackgroundMALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentation
Ruan, J., Xiang, S., Xie, M., Liu, T., & Fu, Y. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995040 · Cited by 400 · see citing works
Al‐masni, M. A., Kim, D., & Kim, T. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105351 · Cited by 385 · see citing works
Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks
Tschandl, P., Rosendahl, C. O., Akay, B. N., Argenziano, G., Blum, A., Braun, R. P., Cabo, H., Gourhant, J.-Y., Kreusch, J. F., Lallas, A., et al. (2018) · JAMA Dermatology · doi:10.1001/jamadermatol.2018.4378 · Cited by 327 · see citing works
Vitiligo: Focus on Clinical Aspects, Immunopathogenesis, and Therapy
Boniface, K., Sénéschal, J., Picardo, M., & Taı̈eb, A. (2017) · Clinical Reviews in Allergy & Immunology · doi:10.1007/s12016-017-8622-7 · Cited by 275 · see citing works
ResGANet: Residual group attention network for medical image classification and segmentation
Cheng, J., Tian, S., Yu, L., Gao, C., Kang, X., Ma, X., Wu, W., Liu, S., & Lu, H. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102313 · Cited by 243 · see citing works
Huang, J., Fang, Y., Wu, Y., Wu, H., Gao, Z., Li, Y., Ser, J. D., Xia, J., & Yang, G. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.051 · Cited by 237 · see citing works
Kroemer, S., Frühauf, J., Campbell, T. M., Massone, C., Schwantzer, G., Soyer, H. P., & Hofmann-Wellenhof, R. (2011) · British Journal of Dermatology · doi:10.1111/j.1365-2133.2011.10208.x · Cited by 202 · see citing works
Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images
Kaur, R., GholamHosseini, H., Sinha, R., & Lindén, M. (2022) · Sensors · doi:10.3390/s22031134 · Cited by 186 · see citing works
Deep Learning in Skin Disease Image Recognition: A Review
Li, L., Wang, X., Hu, W., Xiong, N. N., Du, Y., & Li, B. (2020) · IEEE Access · doi:10.1109/access.2020.3037258 · Cited by 170 · see citing works
Melanoma detection using deep learning-based classifications
Alwakid, G., Gouda, W., Humayun, M., & Sama, N. U. (2022) · Healthcare · doi:10.3390/healthcare10122481 · Cited by 153 · see citing works
Federman, D. G. (1999) · Archives of Family Medicine · doi:10.1001/archfami.8.2.170 · Cited by 136 · cited 1× · see citing works
“As a result, rates of misdiagnosis and underdiagnosis remain high, and diagnostic accuracy ranges from 24 to 70% 4–7 .”
BackgroundDeep learning in dermatology: a systematic review of current approaches, outcomes, and limitations
Jeong, H. K., Park, C., Henao, R., & Kheterpal, M. (2023) · JID Innovations · doi:10.1016/j.xjidi.2022.100150 · Cited by 134 · see citing works
Dermoscopy in vitiligo: diagnosis and beyond
Jha, A. K., Sonthalia, S., Lallas, A., & Chaudhary, R. K. P. (2017) · International Journal of Dermatology · doi:10.1111/ijd.13795 · Cited by 106 · see citing works
ResBCDU-Net: A Deep Learning Framework for Lung CT Image Segmentation
Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., & Anisi, M. H. (2021) · Sensors · doi:10.3390/s21010268 · Cited by 102 · see citing works
Tran, H., Chen, K., Lim, A. C., Jabbour, J., & Shumack, S. (2005) · Australasian Journal of Dermatology · doi:10.1111/j.1440-0960.2005.00189.x · Cited by 92 · see citing works
Zhao, Z., Zeng, Z., Xu, K., Chen, C., & Guan, C. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3052320 · Cited by 88 · see citing works
Menzies, S. W., Sinz, C., Menzies, M., Lo, S., Yolland, W., Lingohr, J., Razmara, M., Tschandl, P., Guitera, P., Scolyer, R. A., et al. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00130-9 · Cited by 82 · see citing works
SwinIR: Image Restoration Using Swin TransformerPreprint
Liang, J., Cao, J., Sun, G., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.10257 · Cited by 81 · see citing works
The utility of dermoscopy in the diagnosis of evolving lesions of vitiligo
Thatte, S., & Khopkar, U. S. (2014) · Indian Journal of Dermatology Venereology and Leprology · doi:10.4103/0378-6323.144144 · Cited by 72 · see citing works
Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment
Hasan, M. M., Islam, M. U., Sadeq, M. J., Fung, W.-K., & Uddin, J. (2023) · Sensors · doi:10.3390/s23010527 · Cited by 68 · see citing works
Guo, L., Yang, Y., Ding, H., Zheng, H., Yang, H., Xie, J., Li, Y., Lin, T., & Ge, Y. (2022) · Annals of Translational Medicine · doi:10.21037/atm-22-1738 · Cited by 62 · see citing works
Design and Assessment of Convolutional Neural Network Based Methods for Vitiligo Diagnosis
Zhang, L., Mishra, S., Zhang, T., Zhang, Y., Zhang, D., Lv, Y., Lv, M., Guan, N., Hu, X. S., Chen, D. Z., et al. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.754202 · Cited by 56 · see citing works
Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review
Hassan, H., Ren, Z., Zhou, C., Khan, M. A., Pan, Y., Zhao, J., & Huang, B. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106731 · Cited by 52 · see citing works
Moreno, G., Tran, H., Chia, A. L., Lim, A., & Shumack, S. (2007) · Australasian Journal of Dermatology · doi:10.1111/j.1440-0960.2007.00340.x · Cited by 52 · see citing works
Adapting content-based image retrieval techniques for the semantic annotation of medical images
Kumar, A., Dyer, S., Kim, J., Li, C., Leong, P. H. W., FULHAM, M. J., & Feng, D. D. (2016) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2016.01.001 · Cited by 52 · see citing works
Self-citationAttention Cost-Sensitive Deep Learning-Based Approach for Skin Cancer Detection and Classification
Ravi, V. (2022) · Cancers · doi:10.3390/cancers14235872 · Cited by 48 · see citing works
Li, Y., Fan, Y., Xu, D., Li, Y., Zhong, Z., Pan, H., Huang, B., Xie, X., Yang, Y., & Liu, B. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2022.1041142 · Cited by 48 · see citing works
(1997) · PubMed · Cited by 44 · see citing works
Peng, L., Wang, C., Tian, G., Liu, G., Gan, L., Lu, Y., Yang, J., Chen, M., & Li, Z. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.995323 · Cited by 38 · see citing works
Evaluation of Facial Vitiligo Severity with a Mixed Clinical and Artificial Intelligence Approach
Hillmer, D., Merhi, R., Boniface, K., Taieb, A., Barnetche, T., Seneschal, J., & Hagedorn, M. (2024) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2023.07.014 · Cited by 37 · see citing works
CT image super-resolution reconstruction based on global hybrid attention
Chi, J., Sun, Z., Wang, H., Lyu, P., Yu, X., & Wu, C. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106112 · Cited by 34 · see citing works
An Intelligent Diagnostic Model for Melasma Based on Deep Learning and Multimode Image Input
Liu, L., Chen, L., Xue, Y., Chen, T., Chen, Y., Lan, Y., Wen, J., Shao, X., & Chen, J. (2022) · Dermatology and Therapy · doi:10.1007/s13555-022-00874-z · Cited by 30 · see citing works
Self-citationWu, W.-J., Lin, S.-W., & Moon, W. K. (2015) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-014-9757-1 · Cited by 29 · see citing works
An effective vitiligo intelligent classification system
Luo, W., Liu, J., Huang, Y., & Zhao, N. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02357-5 · Cited by 26 · see citing works
Reiter, O., Kurtansky, N. R., Nanda, J. K., BUSAM, K. J., Scope, A., Musthaq, S., & Marghoob, A. A. (2021) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.17133 · Cited by 23 · see citing works
Artificial intelligence-based triage of large bowel biopsies can improve workflow
Mayall, F. G., Goodhead, M. D., de Mendonça, L., Brownlie, S. E., Anees, A., & Perring, S. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100181 · Cited by 22 · see citing works
Medical X-ray Images Classification Based on Shape Features and Bayesian Rule
Fesharaki, N. J., & Pourghassem, H. (2012) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn.2012.145 · Cited by 22 · see citing works
Automatic Skin Lesion Segmentation Using GrabCut in HSV Colour SpacePreprint
Tushar, F. I. (2018) · arXiv (Cornell University) · doi:10.13140/rg.2.2.26778.08648 · Cited by 7 · see citing works
Ensemble Pre-Trained Deep Convolutional Neural Network Model for Classifying Medical Image Datasets
Kavitha, S. N., & Inbarani, H. H. (2022) · International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) · doi:10.1109/icaiss55157.2022.10011089 · Cited by 3 · see citing works
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Refined Intelligent Landslide Identification Based on Multi-Source Information Fusion
Wang, X., Wang, D., Liu, C., Zhang, M., Xu, L., Sun, T., Li, W., Cheng, S., & Dong, J. (2024) · Remote Sensing · doi:10.3390/rs16173119 · Cited by 12 · see citing works
Detection of Vitiligo Through Machine Learning and Computer‐Aided Techniques: A Systematic Review
Tanvir, S., Syed, S. A., Hussain, S., Zia, R., Rashid, M., & Zahid, H. (2024) · BioMed Research International · doi:10.1155/bmri/3277546 · Cited by 13 · see citing works
Abdolahnejad, M., Jeong, H., Lin, V., Ng, T., Altaki, E., Mo, A., Yildiz, B., Chan, H. O., Hong, C., & Joshi, R. (2024) · medRxiv · doi:10.1101/2024.09.06.24313068 · Cited by 9 · see citing works
Harikrishnan, N. U., & Sangeetha, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-25893-6 · Cited by 7 · see citing works
Artificial intelligence–enabled precision medicine for inflammatory skin diseases
Tang, A. S., Wei, M. L., Haemel, A., La, C., Sirota, M., & Lee, E. Y. (2026) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2025.10.596 · Cited by 6 · see citing works
Transforming Aesthetic Dermatology: The Role of Artificial Intelligence in Skin Health
Chen, X., Zhou, Z., Ding, H., Zheng, H., & Ge, Y. (2025) · Dermatology and Therapy · doi:10.1007/s13555-025-01459-2 · Cited by 7 · see citing works
Transforming Vitiligo Diagnosis and Treatment Through Artificial Intelligence: A Review
Gholijani, N., Taheri, Z., Bazgiri, M., Darya, G., & Dehghan, Z. (2025) · Scandinavian Journal of Immunology · doi:10.1111/sji.70076 · Cited by 7 · see citing works
Parikh, M., Fang, G., Poon, F., Kyeremeh, M., Cruz, D., Ki, K., Huang, S., Lin, S., Hong, C., & Chan, H. O. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1661554 · Cited by 6 · see citing works
Antor, M. S. U. M., Podder, N. K., Aman, N., Haque, M. R., Arjon, S. S., & Yasmin, T. (2025) · IEEE International Conference on Computing, Applications and Systems (COMPAS) · doi:10.1109/compas67506.2025.11381857 · Cited by 4 · see citing works
Comparative study of dermatologists and deep learning model on diagnosing childhood vitiligo
Yu, S., Chen, Z., He, J., & Wang, H. (2025) · Photodiagnosis and Photodynamic Therapy · doi:10.1016/j.pdpdt.2025.104727 · Cited by 4 · see citing works
AI at the Bedside: Automated Diagnosis, Progression Tracking, and Treatment Forecasting in Vitiligo
Gowda, S. K., Manandhar, K., & Gupta, S. (2025) · Dermatological Reviews · doi:10.1002/der2.70055 · Cited by 3 · see citing works
Wang, Y., Zheng, Y., Yue, C., Zhang, T., Cai, J., Chang, C., Lui, H., Kalia, S., Wang, Z. J., & Lee, T. K. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103920 · Cited by 3 · see citing works
Sapaico-Alberto, A. F., Olaya-Cotera, S., & Flores-Castañeda, R. O. (2024) · Archives of Dermatological Research · doi:10.1007/s00403-024-03650-5 · Cited by 2 · see citing works
Li, J., Yang, Y. Z., Shen, N., & Gao, H. (2025) · International Conference on Computer Vision, Image and Deep Learning (CVIDL) · doi:10.1109/cvidl65390.2025.11085864 · Cited by 2 · see citing works
Artificial intelligence-enabled precision medicine for inflammatory skin diseasesPreprint
Tang, A., Wei, M. L., Haemel, A. K., La, C., Sirota, M., & Lee, E. Y. (2025) · PubMed · doi:10.48550/arxiv.2505.09527 · Cited by 5 · see citing works
Comparsion of CNN and Transformer Models with Transfer Learning in Monkeypox Diagnosis
İspir, F. B., & Tanyıldızı, E. (2025) · International Artificial Intelligence and Data Processing Symposium (IDAP) · doi:10.1109/idap68205.2025.11222339 · Cited by 1 · see citing works
Wittrup, E. M., Kay, A., Rosen, J., Chen, K., & Najarian, K. (2025) · Biomedical Journal · doi:10.1016/j.bj.2025.100933 · Cited by 1 · see citing works
Li, J., Guo, P., Zhang, Y., Ma, H., zhao, Z., Wang, Z., Wang, Z., Chen, Y., Xu, L., Zhang, L., et al. (2026) · Genetics Selection Evolution · doi:10.1186/s12711-025-01015-8 · Cited by 1 · see citing works
Martínez-Ortega, J. I., Hernandez, O. R. J., & Naidnur, S. (2026) · Cureus · doi:10.7759/cureus.112520 · Cited by 1 · see citing works
Detection of Vitiligo Using Ensemble Learning
Jan, M., & Hafiz, A. M. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-83790-6_16
Biswas, R., Sohan, M. F. A. A., Rudro, R. A. M., & Parvin, S. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11670-z
Vitiligo Detection and Progression Analysis Using Vision Transformers and Self-Attention Networks
Gopalakrishnan, B., Sangeethaa, S. N., Padmashree, A., Siamala Devi, S., Lathajothi, V., & Sathishkannan, R. (2025) · International Conference on Networks, Multimedia and Information Technology (NMITCON) · doi:10.1109/nmitcon65824.2025.11188357
Further Directions in Vitiligo
Kothari, A. U., Mokhtari, M., Quiñonez, R. L., Ziglar, J. A., & Hamzavi, I. H. (2026) · Springer eBooks · doi:10.1007/978-981-95-3857-7_25
A Comprehensive Survey on Vitiligo Detection Using Deep Learning
Malge, K., & Manikamma (2026) · ITM Web of Conferences · doi:10.1051/itmconf/20268101022
Akilandasowmya, G., & Joshitha, K. L. (2026) · International Journal of Software Engineering and Knowledge Engineering · doi:10.1142/s0218194026500075
Nand, S., Amjad, U., Raza, A., & Kumari, N. A. S. (2026) · Vietnam Journal of Computer Science · doi:10.1142/s2196888826500077
Stella, M. M., Budiyanto, A., Sari, A. R. P., Dewi, S. T., & Wirohadidjojo, Y. W. (2026) · Indian Journal of Dermatology · doi:10.4103/ijd.ijd_430_25
Vitiligo state assessment based on progressive transfer learning and multimodal domain adaptation
Wu, C., Jiang, S., He, K., Li, Z., Li, S., Wang, K., Zhang, J., & Zhang, J. (2026) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ae6457
Self-citationHe, K., Xu, T., Feng, Y., Lu, Y., Wang, X., Jiang, L., Guo, S., He, Y., Dai, W., Zhang, W., et al. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1853327
Self-citationDevelopment of Deep Learning Model for Hypopigmented Disease Detection
Patle, S. K., & Reddy, K. T. V. (2026) · Algorithms for intelligent systems · doi:10.1007/978-3-032-12243-8_22
Alzu'bi, A. A., Khateeb, S. A., Elzaghmouri, B. M., Alshiyab, D. M., Alasal, S. A., & Zhou, L. (2026) · JMIR Medical Informatics · doi:10.2196/81942 · Cited by 1 · see citing works
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Bansal, S., Mrig, P. A., Chawla, H., Sushama, S., & Karadia, P. (2026) · Indian Dermatology Online Journal · doi:10.4103/idoj.idoj_1220_25
Wang, H., Zhang, J., Cui, T., He, Y., Zhang, W., & Jin, H. (2026) · Dermatology Practical & Conceptual · doi:10.5826/dpc.160s1a7713
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Varadarajan, N., Thamızhanban, D., Tejashwini, P., Sowmya, G., Balakrishnan, A., Raj, P., Chaithanya, S., & Shaju, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0567-5.ch008
AI-Driven Predictive Modeling for Personalized Vitiligo Treatment and Patient Stratification
Khaldy, M. A. A., & Al-Tarawneh, A. A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0567-5.ch014
Ede, N., Khan, F., Vetter, I., Schroeder, C., & Ahmed, A. M. (2026) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2026.09.853
- Authors
- Zhong, F., He, K., Ji, M., Chen, J., Gao, T., Li, S., Zhang, J., & Li, C.
- Affiliations
- Sichuan University (CN)
- Xijing Hospital (CN)
- Corresponding author
- Zhang, Junpeng
- Li, Chunying
- Venue
- Scientific Reports
- Published
- 2024-04-21
- Volume
- 14
- Issue
- 1
- Pages
- 9127-9127
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC11033269
- OpenAlex
- W4394989200
- Cited by
- 37
- References
- 52
- Citations by year
- 2026: 18 · 2025: 15 · 2024: 4
- FWCI
- 6.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China Mathematics Tianyuan Foundation (12126606)
- the R&D project of Pazhou Lab (2023K0605)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › melanin and skin pigmentation
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- vitiligo, skin lesion classification, ResNet, Swin Transformer, model interpretability, class activation maps, feature map visualization, dermatological diagnosis
- MeSH terms
- Deep Learning, Cues, Humans, Electric Power Supplies, Vitiligo, Hypopigmentation
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