Deep learning-based fully automated differential diagnosis of eyelid basal cell and sebaceous carcinoma using whole slide images.

ABSTRACT:

Background

The differential diagnosis of eyelid basal cell carcinoma (BCC) and sebaceous carcinoma (SC) is highly dependent on pathologist's experience. Herein, we proposed a fully automated differential diagnostic method, which used deep learning (DL) to accurately classify eyelid BCC and SC based on whole slide images (WSIs).

Methods

We used 116 haematoxylin and eosin (H&E)-stained sections from 116 eyelid BCC patients and 180 H&E-stained sections from 129 eyelid SC patients treated at the Shanghai Ninth People's Hospital from 2017 to 2019. The method comprises two stages: patch prediction by the DenseNet-161 architecture-based DL model and WSI differentiation by an average-probability strategy-based integration module, and its differential performance was assessed by the

SUBMITTER: Luo Y 

PROVIDER: S-EPMC9338367 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature