ArticleIEEE access : practical innovations, open solutions2021
Scale-Aware Transformers for Diagnosing Melanocytic Lesions.
Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Integrating anisotropic heat flow and transformer encoders in convolutional neural network for skin cancer classification.Frontiers in medicine · 2026Article
- Vision Transformers in Medical Imaging: a Comprehensive Review of Advancements and Applications Across Multiple Diseases.Journal of imaging informatics in medicine · 2025Review
- Embedding-driven dual-branch approach for accurate breast tumor cellularity classification.Scientific reports · 2025Article
- Robust ROI Detection in Whole Slide Images Guided by Pathologists' Viewing Patterns.Journal of imaging informatics in medicine · 2025Article
- Machine learning classification of diagnostic accuracy in pathologists interpreting breast biopsies.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Quilt-1M: One Million Image-Text Pairs for Histopathology.Advances in neural information processing systems · 2023Article
- Transformer-Based Approach to Melanoma Detection.Sensors (Basel, Switzerland) · 2023Article
- Identifying the role of vision transformer for skin cancer-A scoping review.Frontiers in artificial intelligence · 2023Article
- Classification of clinical skin lesions with double-branch networks.Frontiers in medicine · 2023Article
- A Deep CNN Transformer Hybrid Model for Skin Lesion Classification of Dermoscopic Images Using Focal Loss.Diagnostics (Basel, Switzerland) · 2022Article
- Improving the Diagnosis of Skin Biopsies Using Tissue Segmentation.Diagnostics (Basel, Switzerland) · 2022Article
- Automated analysis of whole slide digital skin biopsy images.Frontiers in artificial intelligence · 2022Article
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8 authors.
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Abstract
Diagnosing melanocytic lesions is one of the most challenging areas of pathology with extensive intra- and inter-observer variability. The gold standard for a diagnosis of invasive melanoma is the examination of histopathological whole slide skin biopsy images by an experienced dermatopathologist. Digitized whole slide images offer novel opportunities for computer programs to improve the diagnostic performance of pathologists. In order to automatically classify such images, representations that reflect the content and context of the input images are needed. In this paper, we introduce a novel self-attention-based network to learn representations from digital whole slide images of melanocytic skin lesions at multiple scales. Our model softly weighs representations from multiple scales, allowing it to discriminate between diagnosis-relevant and -irrelevant information automatically. Our experiments show that our method outperforms five other state-of-the-art whole slide image classification methods by a significant margin. Our method also achieves comparable performance to 187 practicing U.S. pathologists who interpreted the same cases in an independent study. To facilitate relevant research, full training and inference code is made publicly available at https://github.com/meredith-wenjunwu/ScATNet.
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