ArticleScientific reports2023
Weakly supervised detection and classification of basal cell carcinoma using graph-transformer on whole slide images.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers.
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14 citing papers in PubMed.
- Detection of basal cell carcinoma on whole-slide images from Mohs micrographic surgery using weakly supervised learning.JAAD international · 2026Article
- Ensemble learning of pathology foundation models for precision oncology.Cancer cell · 2026Article
- nnMIL: a generalizable multiple instance learning framework for computational pathology.Nature biomedical engineering · 2026Article
- Early detection of metastatic risk in primary cutaneous melanoma using weakly supervised learning.Scientific reports · 2026Article
- Cross-slide augmentation for whole slide image classification based on class activation map.Scientific reports · 2025Article
- Enhancing basal cell carcinoma classification in preoperative biopsies via transfer learning with weakly supervised graph transformers.BMC medical imaging · 2025Article
- Self supervised artificial intelligence predicts poor outcome from primary cutaneous squamous cell carcinoma at diagnosis.NPJ digital medicine · 2025Article
- Not seeing the trees for the forest. The impact of neighbours on graph-based configurations in histopathology.BMC bioinformatics · 2025Article
- PathoEye: A deep learning framework for the whole-slide image analysis of skin tissue.Computational and structural biotechnology journal · 2025Article
- TDT-MIL: a framework with a dual-channel spatial positional encoder for weakly-supervised whole slide image classification.Biomedical optics express · 2024Article
- Article
- Deep learning model shows pathologist-level detection of sentinel node metastasis of melanoma and intra-nodal nevi on whole slide images.Frontiers in medicine · 2024Article
- Self-supervised artificial intelligence predicts recurrence, metastasis and disease specific death from primary cutaneous squamous cell carcinoma at diagnosis.Research square · 2023Article
- A survey of Transformer applications for histopathological image analysis: New developments and future directions.Biomedical engineering online · 2023Review
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The high incidence rates of basal cell carcinoma (BCC) cause a significant burden at pathology laboratories. The standard diagnostic process is time-consuming and prone to inter-pathologist variability. Despite the application of deep learning approaches in grading of other cancer types, there is limited literature on the application of vision transformers to BCC on whole slide images (WSIs). A total of 1832 WSIs from 479 BCCs, divided into training and validation (1435 WSIs from 369 BCCs) and testing (397 WSIs from 110 BCCs) sets, were weakly annotated into four aggressivity subtypes. We used a combination of a graph neural network and vision transformer to (1) detect the presence of tumor (two classes), (2) classify the tumor into low and high-risk subtypes (three classes), and (3) classify four aggressivity subtypes (five classes). Using an ensemble model comprised of the models from cross-validation, accuracies of 93.5%, 86.4%, and 72% were achieved on two, three, and five class classifications, respectively. These results show high accuracy in both tumor detection and grading of BCCs. The use of automated WSI analysis could increase workflow efficiency.
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