Improving the Diagnosis of Skin Biopsies Using Tissue Segmentation
Abstract
Invasive melanoma, a common type of skin cancer, is considered one of the deadliest. Pathologists routinely evaluate melanocytic lesions to determine the amount of atypia, and if the lesion represents an invasive melanoma, its stage. However, due to the complicated nature of these assessments, inter- and intra-observer variability among pathologists in their interpretation are very common. Machine-learning techniques have shown impressive and robust performance on various tasks including healthcare. In this work, we study the potential of including semantic segmentation of clinically important tissue structure in improving the diagnosis of skin biopsy images. Our experimental results show a 6% improvement in F-score when using whole slide images along with epidermal nests and cancerous dermal nest segmentation masks compared to using whole-slide images alone in training and testing the diagnosis pipeline.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 14, 2022
- Source ID
- 10.3390/diagnostics12071713
Entities
People
- Beibin Li
- Caitlin J. May
- Joann Elmore
- Linda G. Shapiro
- Mojgan Mokhtari
- Oliver H. Chang
- Shima Nofallah
- Stevan Knezevich
- Wenjun Wu
Organizations
- Melanoma Research Alliance
- National Cancer Institute
- United States Department of Defense