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Scale-Aware Transformers for Diagnosing Melanocytic Lesions.

Authors :
Wu W
Mehta S
Nofallah S
Knezevich S
May CJ
Chang OH
Elmore JG
Shapiro LG
Source :
IEEE access : practical innovations, open solutions [IEEE Access] 2021; Vol. 9, pp. 163526-163541. Date of Electronic Publication: 2021 Dec 06.
Publication Year :
2021

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.

Details

Language :
English
ISSN :
2169-3536
Volume :
9
Database :
MEDLINE
Journal :
IEEE access : practical innovations, open solutions
Publication Type :
Academic Journal
Accession number :
35211363
Full Text :
https://doi.org/10.1109/ACCESS.2021.3132958