{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:43:12Z","timestamp":1760035392191,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T00:00:00Z","timestamp":1752105600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Integrating artificial intelligence into clinical photography offers great potential for monitoring skin conditions such as actinic keratosis (AK) and skin field cancerization. Identifying the extent of AK lesions often requires more than analyzing lesion morphology\u2014it also depends on contextual cues, such as surrounding photodamage. This highlights the need for models that can combine fine-grained local features with a comprehensive global view. Methods: To address this challenge, we propose AKTransU-net, a hybrid U-net-based architecture. The model incorporates Transformer blocks to enrich feature representations, which are passed through ConvLSTM modules within the skip connections. This configuration allows the network to maintain semantic coherence and spatial continuity in AK detection. This global awareness is critical when applying the model to whole-image detection via tile-based processing, where continuity across tile boundaries is essential for accurate and reliable lesion segmentation. Results: The effectiveness of AKTransU-net was demonstrated through comparative evaluations with state-of-the-art segmentation models. A proprietary annotated dataset of 569 clinical photographs from 115 patients with actinic keratosis was used to train and evaluate the models. From each photograph, crops of 512 \u00d7 512 pixels were extracted using translation lesion boxes that encompassed lesions in different positions and captured different contexts. AKtransU-net exhibited a more robust context awareness and achieved a median Dice score of 65.13%, demonstrating significant progress in whole-image assessments. Conclusions: Transformer-driven context modeling offers a promising approach for robust AK lesion monitoring, supporting its application in real-world clinical settings where accurate, context-aware analysis is crucial for managing skin field cancerization.<\/jats:p>","DOI":"10.3390\/diagnostics15141752","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T15:36:58Z","timestamp":1752161818000},"page":"1752","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A\u039atransU-Net: Transformer-Equipped U-Net Model for Improved Actinic Keratosis Detection in Clinical Photography"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0949-4776","authenticated-orcid":false,"given":"Panagiotis","family":"Derekas","sequence":"first","affiliation":[{"name":"Department of Medical Physics, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Charalampos","family":"Theodoridis","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, School of Engineering, University of Ioannina, 45110 Ioannina, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aristidis","family":"Likas","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, School of Engineering, University of Ioannina, 45110 Ioannina, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0252-8314","authenticated-orcid":false,"given":"Ioannis","family":"Bassukas","sequence":"additional","affiliation":[{"name":"Department of Skin and Venereal Diseases, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5482-1304","authenticated-orcid":false,"given":"Georgios","family":"Gaitanis","sequence":"additional","affiliation":[{"name":"Department of Skin and Venereal Diseases, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7882-2686","authenticated-orcid":false,"given":"Athanasia","family":"Zampeta","sequence":"additional","affiliation":[{"name":"Department of Skin and Venereal Diseases, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7487-6103","authenticated-orcid":false,"given":"Despina","family":"Exadaktylou","sequence":"additional","affiliation":[{"name":"Department of Dermatology, General Hospital of Nikaia\u2014Piraeus \u201cAgios Panteleimon\u201d, 18454 Nikaia, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5021-6577","authenticated-orcid":false,"given":"Panagiota","family":"Spyridonos","sequence":"additional","affiliation":[{"name":"Department of Medical Physics, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/j.jaad.2020.03.126","article-title":"Jambusaria-Pahlajani, Field cancerization: Definition, epidemiology, risk factors, and outcomes","volume":"83","author":"Willenbrink","year":"2020","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1111\/jdv.14652","article-title":"Defining the actinic keratosis field: A literature review and discussion","volume":"32","author":"Nart","year":"2018","journal-title":"J. Eur. Acad. Dermatol. Venereol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"845","DOI":"10.36849\/JDD.6704","article-title":"Classifying Actinic Keratosis: What the Reality of Everyday Clinical Practice Shows Us","volume":"21","author":"Schmitz","year":"2022","journal-title":"J. Drugs Dermatol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1111\/jdv.19559","article-title":"Actinic keratosis: Current challenges and unanswered questions","volume":"38","author":"Malvehy","year":"2024","journal-title":"J. Eur. Acad. Dermatol. Venereol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"adv00358","DOI":"10.2340\/00015555-3690","article-title":"Systematic Literature Review and Network Meta-analysis of the Efficacy and Acceptability of Interventions in Actinic Keratoses","volume":"101","author":"Ezzedine","year":"2021","journal-title":"Acta Derm. Venereol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"141","DOI":"10.2165\/00128071-200405030-00001","article-title":"Quantifying actinic keratosis: Assessing the evidence","volume":"5","author":"Epstein","year":"2004","journal-title":"Am. J. Clin. Dermatol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1144","DOI":"10.1111\/ced.15136","article-title":"Method of Assessing Skin Cancerization and KeratosesTM (MASCKTM): Development and photographic validation in multiple anatomical sites of a novel assessment tool intended for clinical evaluation of patients with extensive skin field cancerization","volume":"47","author":"Baker","year":"2022","journal-title":"Clin. Exp. Dermatol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pasquali, P. (2020). Photography in Clinical Medicine, Springer. [1st ed.].","DOI":"10.1007\/978-3-030-24544-3"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hames, S.C., Sinnya, S., Tan, J.M., Morze, C., Sahebian, A., Soyer, H.P., and Prow, T.W. (2015). Automated detection of actinic keratoses in clinical photographs. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0112447"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.compbiomed.2017.07.001","article-title":"Automatic discrimination of actinic keratoses from clinical photographs","volume":"88","author":"Spyridonos","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"47","DOI":"10.3934\/ElectrEng.2020.1.47","article-title":"Deep learning for actinic keratosis classification","volume":"4","author":"Nanni","year":"2020","journal-title":"AIMS Electron. Electr. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1111\/srt.12684","article-title":"Late fusion of deep and shallow features to improve discrimination of actinic keratosis from normal skin using clinical photography","volume":"25","author":"Spyridonos","year":"2019","journal-title":"Skin. Res. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Spyridonos, P., Gaitanis, G., Likas, A., and Bassukas, I.D. (2023). A convolutional neural network based system for detection of actinic keratosis in clinical images of cutaneous field cancerization. Biomed. Signal Process Control., 79.","DOI":"10.1016\/j.bspc.2022.104059"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Derekas, P., Spyridonos, P., Likas, A., Zampeta, A., Gaitanis, G., and Bassukas, I. (2023). The Promise of Semantic Segmentation in Detecting Actinic Keratosis Using Clinical Photography in the Wild. Cancers, 15.","DOI":"10.20944\/preprints202309.0085.v1"},{"key":"ref_15","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., and Shao, L. (2021, January 11\u201317). Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Graham, B., El-Nouby, A., Touvron, H., Stock, P., Joulin, A., J\u00e9gou, H., and Douze, M. (2021, January 11\u201317). LeViT: A Vision Transformer in ConvNet\u2019s Clothing for Faster Inference. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.01204"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","article-title":"U-net and its variants for medical image segmentation: A review of theory and applications","volume":"9","author":"Siddique","year":"2021","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"10076","DOI":"10.1109\/TPAMI.2024.3435571","article-title":"Medical Image Segmentation Review: The Success of U-Net","volume":"46","author":"Azad","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","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. Biomed. Eng. Online, 23.","DOI":"10.1186\/s12938-024-01212-4"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8747","DOI":"10.21037\/qims-23-542","article-title":"Transformers in medical image segmentation: A narrative review","volume":"13","author":"Khan","year":"2023","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xiao, H., Li, L., Liu, Q., Zhu, X., and Zhang, Q. (2023). Transformers in medical image segmentation: A review. Biomed. Signal Process Control., 84.","DOI":"10.1016\/j.bspc.2023.104791"},{"key":"ref_24","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., and Zhou, Y. (2021). TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"103280","DOI":"10.1016\/j.media.2024.103280","article-title":"TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers","volume":"97","author":"Chen","year":"2024","journal-title":"Med. Image Anal."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Atek, S., Mehidi, I., Jabri, D., and Belkhiat, D.E.C. (2022, January 8\u20139). SwinT-Unet: Hybrid architecture for Medical Image Segmentation Based on Swin transformer block and Dual-Scale Information. Proceedings of the 2022 7th International Conference on Image and Signal Processing and Their Applications (ISPA), Mostaganem, Algeria.","DOI":"10.1109\/ISPA54004.2022.9786367"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4005615","DOI":"10.1109\/TIM.2022.3178991","article-title":"DS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation","volume":"71","author":"Lin","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_28","first-page":"2441","article-title":"UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-Wise Perspective with Transformer","volume":"36","author":"Wang","year":"2022","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Fu, B., Peng, Y., He, J., Tian, C., Sun, X., and Wang, R. (2024). HmsU-Net: A hybrid multi-scale U-net based on a CNN and transformer for medical image segmentation. Comput. Biol. Med., 170.","DOI":"10.1016\/j.compbiomed.2024.108013"},{"key":"ref_30","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","volume":"Volume 9351","author":"Ronneberger","year":"2015","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","article-title":"MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation","volume":"121","author":"Ibtehaz","year":"2020","journal-title":"Neural Netw."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, S., Zou, Y., and Liu, P.X. (2021). IBA-U-Net: Attentive BConvLSTM U-Net with Redesigned Inception for medical image segmentation. Comput. Biol. Med., 135.","DOI":"10.1016\/j.compbiomed.2021.104551"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, X., Yang, S., Jiang, Y., Chen, Y., and Sun, F. (2024). FAFS-UNet: Redesigning skip connections in UNet with feature aggregation and feature selection. Comput. Biol. Med., 170.","DOI":"10.1016\/j.compbiomed.2024.108009"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Byeon, W., Breuel, T.M., Raue, F., and Liwicki, M. (2015, January 7\u201312). Scene labeling with LSTM recurrent neural networks. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298977"},{"key":"ref_35","first-page":"802","article-title":"Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting","volume":"28","author":"Shi","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_36","first-page":"406","article-title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","volume":"2019","author":"Azad","year":"2019","journal-title":"Int. Conf. Comput. Vis. Workshop"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jiang, X., Jiang, J., Wang, B., Yu, J., and Wang, J. (2022). SEACU-Net: Attentive ConvLSTM U-Net with squeeze-and-excitation layer for skin lesion segmentation. Comput. Methods Programs Biomed., 225.","DOI":"10.1016\/j.cmpb.2022.107076"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1016\/0190-9622(95)91813-2","article-title":"Polarized light photography in the evaluation of photoaging","volume":"33","author":"Muccini","year":"1995","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_39","unstructured":"(2019). Photography\u2014Digital Still Cameras\u2014Determination of Exposure Index, ISO Speed Ratings, Standard Output Sensitivity, and Recommended Exposure Index (Standard No. ISO 12232:2019)."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, ECCV.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Prokopiou, I., and Spyridonos, P. (2025). Highlighting the Advanced Capabilities and the Computational Efficiency of DeepLabV3+ in Medical Image Segmentation: An Ablation Study. BioMedInformatics, 5.","DOI":"10.3390\/biomedinformatics5010010"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Varghese, R., and Sambath, M. (2024, January 18\u201319). YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness. Proceedings of the 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS), Chennai, India.","DOI":"10.1109\/ADICS58448.2024.10533619"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zou, Y., Tan, Y., and Zhou, C. (2024). YOLOv8-seg-CP: A lightweight instance segmentation algorithm for chip pad based on improved YOLOv8-seg model. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-78578-x"},{"key":"ref_44","unstructured":"Explore Ultralytics YOLOv8 (2025, May 29). Ultralytics YOLO Docs (n.d.). Available online: https:\/\/docs.ultralytics.com\/models\/yolov8\/."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/14\/1752\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:08:06Z","timestamp":1760033286000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/14\/1752"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,10]]},"references-count":44,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["diagnostics15141752"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15141752","relation":{},"ISSN":["2075-4418"],"issn-type":[{"type":"electronic","value":"2075-4418"}],"subject":[],"published":{"date-parts":[[2025,7,10]]}}}