Vision Transformers for Low-Quality Histopathological Images: A Case Study on Squamous Cell Carcinoma Margin Classification
Open accessAbstract
Background/Objectives: Squamous cell carcinoma (SCC), a prevalent form of skin cancer, presents diagnostic challenges, particularly in resource-limited settings with a low-quality imaging infrastructure. The accurate classification of SCC margins is essential to guide effective surgical interventions and reduce recurrence rates. This study proposes a vision transformer (ViT)-based model to improve SCC margin classification by addressing the limitations of convolutional neural networks (CNNs) in analyzing low-quality histopathological images. Methods: This study introduced a transfer learning approach using a ViT architecture customized with additional flattening, batch normalization, and dense layers to enhance its capability for SCC margin classification. A performance evaluation was conducted using machine learning metrics averaged over five-fold cross-validation and comparisons were made with the leading CNN models. Ablation studies have explored the effects of architectural configuration on model performance. Results: The ViT-based model achieved superior SCC margin classification with 0.928 ± 0.027 accuracy and 0.927 ± 0.028 AUC, surpassing the highest performing CNN model, InceptionV3 (accuracy: 0.86 ± 0.049; AUC: 0.837 ± 0.029), demonstrating robustness of ViT over CNN for low-quality histopathological images. Ablation studies have reinforced the importance of tailored architectural configurations for enhancing diagnostic performance. Conclusions: This study underscores the transformative potential of ViTs in histopathological analysis, especially in resource-limited settings. By enhancing diagnostic accuracy and reducing dependence on high-quality imaging and specialized expertise, it presents a scalable solution for global cancer diagnostics. Future research should prioritize optimizing ViTs for such environments and broadening their clinical applications.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Vision transformers (ViTs) are more recently used for pathology and leverage attention mechanisms.
“Vision transformers (ViTs): More recently used for pathology and leveraging attention mechanisms [63].”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [63]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
1 retracted
RETRACTED: The effect of image resolution on convolutional neural networks in breast ultrasound
Tang, S., Chen, J., Jiang, Y., Yang, K., Huang, Z., Wu, H., Cui, C., Shi, S., Ye, X., Tian, H., et al. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e19253 · Cited by 16 · see citing works
RetractedHistopathological Image Analysis: A Review
Gurcan, M. N., Boucheron, L. E., Can, A., Madabhushi, A., Rajpoot, N. M., & Yener, B. (2009) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2009.2034865 · Cited by 2,052 · see citing works
Machine Learning Methods for Histopathological Image Analysis
Komura, D., & Ishikawa, S. (2018) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2018.01.001 · Cited by 937 · see citing works
Do Vision Transformers See Like Convolutional Neural Networks?
Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C., & Dosovitskiy, A. (2021) · arXiv · doi:10.48550/arxiv.2108.08810 · Cited by 663 · see citing works
Maurício, J., Domingues, I., & Bernardino, J. (2023) · Applied Sciences · doi:10.3390/app13095521 · Cited by 617 · see citing works
Digital pathology and artificial intelligence in translational medicine and clinical practice
Baxi, V., Edwards, R., Montalto, M., & Saha, S. (2022) · Modern Pathology · doi:10.1038/s41379-021-00919-2 · Cited by 592 · see citing works
Hasan, N., Nadaf, A., Imran, M., Jiba, U., Sheikh, A., Almalki, W. H., Almujri, S. S., Mohammed, Y., Kesharwani, P., & Ahmad, F. J. (2023) · Molecular Cancer · doi:10.1186/s12943-023-01854-3 · Cited by 358 · see citing works
Deep learning-enabled virtual histological staining of biological samples
Bai, B., Yang, X., Li, Y., Zhang, Y., Pillar, N., & Özcan, A. (2023) · Light Science & Applications · doi:10.1038/s41377-023-01104-7 · Cited by 352 · see citing works
Principles and approaches for reproducible scoring of tissue stains in research
Meyerholz, D. K., & Beck, A. P. (2018) · Laboratory Investigation · doi:10.1038/s41374-018-0057-0 · Cited by 350 · see citing works
McGenity, C., Clarke, E. L., Jennings, C., Matthews, G., Cartlidge, C., Freduah-Agyemang, H., Stocken, D. D., & Treanor, D. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01106-8 · Cited by 290 · see citing works
Synthetic data generation methods in healthcare: A review on open-source tools and methods
Pezoulas, V. C., Zaridis, D. I., Mylona, E., Androutsos, C., Apostolidis, K., Tachos, N. S., & Fotiadis, D. I. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.07.005 · Cited by 270 · see citing works
Skin cancer biology and barriers to treatment: Recent applications of polymeric micro/nanostructures
Khan, N. H., Mir, M., Qian, L., Baloch, M., Khan, M. F. A., Rehman, A. U., Ngowi, E. E., Wu, D., & Ji, X. (2021) · Journal of Advanced Research · doi:10.1016/j.jare.2021.06.014 · Cited by 239 · see citing works
Overview of skin cancer types and prevalence rates across continents
Roky, A. H., Islam, M. M., Ahasan, A. M. F., Mostaq, M. S., Mahmud, M. Z., Amin, M. N., & Mahmud, M. A. (2025) · Cancer Pathogenesis and Therapy · doi:10.1016/j.cpt.2024.08.002 · Cited by 239 · see citing works
Age-Specific Incidence of Melanoma in the United States
Paulson, K. G., Gupta, D., Kim, T. S., Veatch, J. R., Byrd, D. R., Bhatia, S., Wojcik, K. Y., Chapuis, A. G., Thompson, J. A., Madeleine, M. M., et al. (2019) · JAMA Dermatology · doi:10.1001/jamadermatol.2019.3353 · Cited by 231 · see citing works
Islam, T., Hafiz, M. S., Jim, J. R., Kabir, M. M., & Mridha, M. F. (2024) · Healthcare Analytics · doi:10.1016/j.health.2024.100340 · Cited by 216 · see citing works
Patch-based system for Classification of Breast Histology images using deep learning
Roy, K., Banik, D., Bhattacharjee, D., & Nasipuri, M. (2018) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2018.11.003 · Cited by 206 · see citing works
Integrating digital pathology into clinical practice
Hanna, M. G., Ardon, O., Reuter, V. E., Sirintrapun, S. J., England, C., Klimstra, D. S., & Hameed, M. (2021) · Modern Pathology · doi:10.1038/s41379-021-00929-0 · Cited by 157 · see citing works
Hoque, M. Z., Keskinarkaus, A., Nyberg, P., & Seppänen, T. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101997 · Cited by 153 · see citing works
Oral squamous cell carcinoma detection using EfficientNet on histopathological images
Albalawi, E. M., Thakur, A., Ramakrishna, M. T., Bhatia, S., SankaraNarayanan, S., Almarri, B. A., & Aldhyani, T. H. H. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2023.1349336 · Cited by 102 · see citing works
Atabansi, C. C., Nie, J., Liu, H., Song, Q., Yan, L., & Zhou, X. (2023) · BioMedical Engineering OnLine · doi:10.1186/s12938-023-01157-0 · Cited by 99 · see citing works
Automated quality assessment of large digitised histology cohorts by artificial intelligence
Haghighat, M., Browning, L., Sirinukunwattana, K., Malacrino, S., Alham, N. K., Colling, R. T., Cui, Y., Rakha, E. A., Hamdy, F. C., Verrill, C. L., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-08351-5 · Cited by 89 · see citing works
Current status of skin cancers with a focus on immunology and immunotherapy
Kohnehshahri, M. K., Sarkesh, A., Khosroshahi, L. M., HajiEsmailPoor, Z., Aghebati‐Maleki, A., Yousefi, M., & Maleki, L. A. (2023) · Cancer Cell International · doi:10.1186/s12935-023-03012-7 · Cited by 86 · see citing works
Deep Learning Approaches in Histopathology
Ahmed, A. A., Abouzid, M., & Kaczmarek, E. (2022) · Cancers · doi:10.3390/cancers14215264 · Cited by 82 · see citing works
Textural pattern classification for oral squamous cell carcinoma
Rahman, T. Y., Mahanta, L. B., Chakraborty, C., DAS, A. K., & Sarma, J. D. (2017) · Journal of Microscopy · doi:10.1111/jmi.12611 · Cited by 81 · see citing works
Image Analysis in Histopathology and Cytopathology: From Early Days to Current Perspectives
Mezei, T., Kolcsár, M., Joó, A., & Gurzu, S. (2024) · Journal of Imaging · doi:10.3390/jimaging10100252 · Cited by 64 · see citing works
Hu, C., Sapkota, B., Thomasson, J. A., & Bagavathiannan, M. (2021) · Remote Sensing · doi:10.3390/rs13112140 · Cited by 62 · see citing works
Multistage transfer learning for medical images
Ayana, G., Dese, K., Abagaro, A. M., Jeong, K. C., Yoon, S., & Choe, S. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10855-7 · Cited by 61 · see citing works
Self-citationOvercoming the limitations of patch-based learning to detect cancer in whole slide images
Ciga, O., Xu, T., Nofech‐Mozes, S., Noy, S., Lu, F., & Martel, A. L. (2021) · Scientific Reports · doi:10.1038/s41598-021-88494-z · Cited by 59 · see citing works
Colour adaptive generative networks for stain normalisation of histopathology images
Cong, C., Liu, S., Ieva, A. D., Pagnucco, M., Berkovsky, S., & Song, Y. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102580 · Cited by 58 · see citing works
Skin Cancer in U.S. Elderly Adults: Does Life Expectancy Play a Role in Treatment Decisions?
Linos, E., Chren, M., Cenzer, I. S., & Covinsky, K. E. (2016) · Journal of the American Geriatrics Society · doi:10.1111/jgs.14202 · Cited by 48 · see citing works
Shavlokhova, V., Sandhu, S., Flechtenmacher, C., Kövesházi, I., Neumeier, F., Padrón‐Laso, V., Jonke, Ž., Saravi, B., Vollmer, M., Vollmer, A., et al. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10225326 · Cited by 47 · see citing works
Digital staining in optical microscopy using deep learning - a review
Kreiss, L. A., Jiang, S., Li, X., Xu, S., Zhou, K. C., Lee, K. C., Mühlberg, A., Kim, K., Chaware, A., Ando, D. M., et al. (2023) · PhotoniX · doi:10.1186/s43074-023-00113-4 · Cited by 47 · see citing works
Salvi, M., Branciforti, F., Molinari, F., & Meiburger, K. M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.123105 · Cited by 45 · see citing works
Hetz, M. J., Bucher, T.-C., & Brinker, T. J. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103149 · Cited by 39 · see citing works
Pathological Insights: Enhanced Vision Transformers for the Early Detection of Colorectal Cancer
Ayana, G., Barki, H., & Choe, S. (2024) · Cancers · doi:10.3390/cancers16071441 · Cited by 30 · see citing works
Self-citationAsaf, M. Z., Rao, B. K., Akram, M. U., Khawaja, S. G., Khan, S., Truong, T. M., Sekhon, P., Khan, I. J., & Abbasi, M. S. (2024) · Scientific Reports · doi:10.1038/s41598-024-52833-7 · Cited by 28 · see citing works
Wako, B. D., Dese, K., Ulfata, R. E., Nigatu, T. A., Turunbedu, S. K., & Kwa, T. (2022) · Cancer Control · doi:10.1177/10732748221132528 · Cited by 27 · see citing works
Self-citationRegistered multi-device/staining histology image dataset for domain-agnostic machine learning models
Ochi, M., Komura, D., Onoyama, T., Shinbo, K., Endo, H., Odaka, H., Kakiuchi, M., Katoh, H., Ushiku, T., & Ishikawa, S. (2024) · Scientific Data · doi:10.1038/s41597-024-03122-5 · Cited by 24 · see citing works
Madusanka, N., Jayalath, P., Fernando, D., Yasakethu, S. L. P., & Lee, B.-I. (2023) · Cancers · doi:10.3390/cancers15164144 · Cited by 23 · see citing works
Ayana, G., Lee, E., & Choe, S. (2023) · American Journal Of Pathology · doi:10.1016/j.ajpath.2023.11.015 · Cited by 21 · see citing works
Self-citationRare Head and Neck Cancers and Pathological Diagnosis Challenges: A Comprehensive Literature Review
Filippini, D. M., Carosi, F., Querzoli, G., Fermi, M., Ricciotti, I., Molteni, G., Presutti, L., Foschini, M. P., & Locati, L. D. (2024) · Diagnostics · doi:10.3390/diagnostics14212365 · Cited by 21 · see citing works
Levy, J. J., Davis, M. J., Chacko, R. S., Davis, M. J., Fu, L. J., Goel, T., Pamal, A., Nafi, I., Angirekula, A., Suvarna, A., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-023-00477-7 · Cited by 20 · see citing works
Verdaguer‐Faja, J., Toll, A., Boada, A., Guerra‐Amor, Á., Ferrándiz‐Pulido, C., & Jaka, A. (2024) · Cancers · doi:10.3390/cancers16030664 · Cited by 18 · see citing works
Kaczmarzyk, J., Gupta, R., Kurç, T., Abousamra, S., Saltz, J. H., & Koo, P. K. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107631 · Cited by 16 · see citing works
Dettrick, A. J., Foden, N., Hogan, D. B., Azer, M., Blazak, J., Atwell, D., Buddle, N., Min, M., Livingston, R. J., Banney, L. A., et al. (2024) · Pathology · doi:10.1016/j.pathol.2024.05.002 · Cited by 15 · see citing works
Luan, H., Yang, K., Hu, T., Hu, J., Liu, S., Li, R., He, J., Yan, R., Guo, X., Qian, N., et al. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.107578 · Cited by 15 · see citing works
Mohs Micrographic Surgery for Cutaneous Squamous Cell Carcinoma
Zürcher, S., Martignoni, Z., Hunger, R. E., Benzaquen, M., & Jafari, S. M. S. (2024) · Cancers · doi:10.3390/cancers16132394 · Cited by 13 · see citing works
Domain and Histopathology Adaptations–Based Classification for Malignancy Grading System
Mudeng, V., Farid, M. N., Ayana, G., & Choe, S. (2023) · American Journal Of Pathology · doi:10.1016/j.ajpath.2023.07.007 · Cited by 12 · see citing works
Self-citationMixPatch: A New Method for Training Histopathology Image Classifiers
Park, Y., Kim, M., Ashraf, M., Ko, Y. S., & Yi, M. Y. (2021) · Diagnostics · doi:10.3390/diagnostics12061493 · Cited by 9 · see citing works
Image Quality Assessment Using Convolutional Neural Network in Clinical Skin Images
Jeong, H. K., Park, C., Jiang, S. W., Nicholas, M. W., Chen, S. C., Henao, R., & Kheterpal, M. (2024) · JID Innovations · doi:10.1016/j.xjidi.2024.100285 · Cited by 9 · see citing works
Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology
Guichemerre, A., Belharbi, S., Mayet, T., Murtaza, S., Shamsolmoali, P., McCaffrey, L. M., & Granger, É. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00008 · Cited by 8 · see citing works
Xu, S., Xiang, S., Meng, F., & Wu, Q. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.055157 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [39]Li H. Zhang Y. Chen P. Shui Z. Zhu C. Yang L. Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysis arXiv 2024 2410.14195
- [42]Rahat F. Hossain M.S. Ahmed M.R. Jha S.K. Ewetz R. Data Augmentation for Image Classification Using Generative AI arXiv 2024 2409.00547
Cited by
Sorted by citations
Alwahaibi, N., & Alwahaibi, M. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1678753 · Cited by 8 · see citing works
Borra, S., Dey, N., Fong, S., Sherratt, R. S., & Shi, F. (2026) · Cancers · doi:10.3390/cancers18091361 · Cited by 8 · see citing works
Ahmad, M. M. (2025) · Electronic Journal of General Medicine · doi:10.29333/ejgm/17046 · Cited by 6 · see citing works
Makina, M., Chacha, W. M., & Mwangi, R. W. (2026) · Engineering Reports · doi:10.1002/eng2.70655 · Cited by 4 · see citing works
Multimodal data analysis reveals asynchronous aging dynamics across female reproductive organs
Soldatkina, O., Pedro, L. V.-S., Pujol‐Gualdo, N., Hommad, A. E., Ramírez, J. M., Ripoll-Cladellas, A., Sopena-Rios, M., Tabares, D., Pérez-Elena, M. Á., Torrents, D., et al. (2026) · Nature Aging · doi:10.1038/s43587-026-01098-y · Cited by 2 · see citing works
LGPNet: A dual-branch parallel network for cervical cell image classification
Xin, Y., Li, Q., & Li, D. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109076 · Cited by 2 · see citing works
Deep Learning Algorithms to Analyze Medical Images for Early Detection of Cancer
Saini, C., Sakshi, Singh, O., Pandey, C., & Singh, S. (2025) · Springer eBooks · doi:10.1007/978-981-96-9346-7_16 · Cited by 2 · see citing works
Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature
Abu-Dayeh, A., Cazzato, G., & Giubellino, A. (2026) · Diagnostics · doi:10.3390/diagnostics16111702 · Cited by 1 · see citing works
AI-Driven Transfer Learning for Medical Imaging and Sensor-Based Healthcare
Assudani, P. J., Leema, A. A., Balakrishnan, P., Kollem, S., Pachlor, R., López‐Linares, K., Oliver, I. M., Haick, H., & Bhaiyya, M. L. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10667-8 · Cited by 2 · see citing works
Automated quality evaluation of Cervical Cytopathology Whole Slide Images based on content analysis
Kang, L., Wang, J., Qin, J., Liang, Y., & He, Y. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109419 · Cited by 1 · see citing works
Comparative Analysis of Hybrid CNN-ViT and CNN for Brain Tumor Classification
Fauzi, A., Fuadi, A. L., & Yunial, A. H. (2026) · JISKA (Jurnal Informatika Sunan Kalijaga) · doi:10.14421/jiska.5860 · Cited by 1 · see citing works
Hasona, R. F., Saafan, M. M., El-Gendy, E. M., Ghanam, W., & MOHAMED, W. Z. (2026) · Microorganisms · doi:10.3390/microorganisms14102207 · Cited by 1 · see citing works
Dual-Attention Enhanced Diffusion-Based Medical Image Diagnosis
Zhou, H., & Wang, T. (2025) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc68654.2025.11438235
Liu, L., Yin, F., Yang, G., Li, Q., Li, S., Pan, T., Liu, T., Tang, J., Ming, R., Song, Y. Q., et al. (2026) · Computer Science and Information Systems · doi:10.2298/csis251130024l
Rao, S., Chilukuri, P., Shetty, S., M, S. S., K, B. M., & Tripathi, S. (2026) · IEEE International Colloquium on Signal Processing & Its Applications (CSPA) · doi:10.1109/cspa68262.2026.11517801
Sahu, P., Bharany, S., Singh, J., Mohamed, H. G., & Rehman, A. U. (2026) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/0115734056437477260427145329
Swapna, B., & Reddy, P. C. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-22118-6_16
Artificial intelligence (imaging analysis) fundamentals
Akbar, A., & Niazi, M. K. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40383-5.00006-6
Data-efficient image transformer (DeiT) for high accuracy lung cancer histopathology classification
Hussain, S., Wenhui, Y., Hussain, B., Hussain, E., & Jin, H. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21954-8
- Authors
- Park, S.-Y., Ayana, G., Wako, B. D., Jeong, K. C., Yoon, S.-D., & Choe, S.-W.
- Affiliations
- Kumoh National Institute of Technology (KR)
- Jimma University (ET)
- University of Florida (US)
- Chonnam National University (KR)
- Corresponding author
- Yoon, Soon-Do
- Choe, Se-woon
- Venue
- Diagnostics
- Publisher
- MDPI AG
- Published
- 2025-01-23
- Volume
- 15
- Issue
- 3
- Pages
- 260
- ISSN
- 2075-4418 (ISSN-L)
- PMC
- PMC11817517
- OpenAlex
- W4406765327
- Cited by
- 18
- References
- 52
- Citations by year
- 2026: 12 · 2025: 6
- FWCI
- 7.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Diagnostics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Research Foundation of Korea (NRF) (RS-2023-00240521)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- vision transformer, squamous cell carcinoma, histopathological images, low-quality medical images, transfer learning, skin cancer diagnosis, resource-limited settings, CNN comparison, ablation study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
