Retrosynthetic analysis via deep learning to improve pilomatricoma diagnoses
Our pilomatricoma study demonstrates the efficacy of a deep learning-based histopathological diagnosis model, as validated by its high performance across various scales, and it is enhanced by an innovative retrosynthetic approach for saliency mapping.
Authors
- Xinyu Tan (ORCID: https://orcid.org/0000-0001-6849-0606)
- Jianglin Zhang (ORCID: https://orcid.org/0000-0003-0434-1729)
- Kaibin Lin (ORCID: https://orcid.org/0000-0003-1973-9537)
- Hongyang Fu
- Hui Hu (ORCID: https://orcid.org/0000-0001-8198-6374)
- Zheng Wang (ORCID: https://orcid.org/0000-0002-1343-5199)
- Chong Wang (ORCID: https://orcid.org/0009-0001-6356-9101)
- Yang Xue (ORCID: https://orcid.org/0009-0002-6454-0682)
Institutions
- Jinan University (CN)
- Southern University of Science and Technology (CN)
- Southern Medical University Shenzhen Hospital (CN)
- Shenzhen Second People's Hospital (CN)
- Hunan First Normal University (CN)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2024-09-19
- DOI
- https://doi.org/10.1016/j.compbiomed.2024.109152
- Citations
- 3
- Primary Topic
- Cancer and Skin Lesions
- Type
- article
- Field-Weighted Citation Impact
- 1.60