A Two-Stage End-to-End Deep Learning Framework for Pathologic Examination in Skin Tumor Diagnosis

Neurofibromas (NFs), Bowen disease (BD), and seborrheic keratosis (SK) are common skin tumors. Pathologic examination is the gold standard for diagnosis of these tumors. Current pathologic diagnosis is primarily based on microscopic observation, which is laborious and time-consuming. With digitization, artificial intelligence can be used to improve the efficiency of pathologic diagnosis. This research aims to develop an end-to-end extendable framework for the diagnosis of skin tumor based on pathologic slide images. NF, BD, and SK were selected as target skin tumors. A two-stage skin cancer diagnosis framework is proposed in this article, which consists of two parts: patches-wise diagnosis, and slide-wise diagnosis. Patches-wise diagnosis compares different convolutional neural networks to extract features and distinguish categories from patches generated in whole slide images. Slide-wise diagnosis combines attention graph gated network model prediction with post-processing algorithm. This approach can fuse information from feature-embedding learning and domain knowledge to draw conclusions. Training, validation, and testing were performed on NF, BD, SK, and negative samples. Accuracy and receiver operating characteristic curves were used to evaluate the classification performance. This study investigated the feasibility of skin tumor diagnosis from pathologic images and may be the first instance of applying deep learning to address these three types of tumor diagnoses in skin pathology.