Objective To construct and validate a deep convolutional neural network (DCNN)‐based
artificial intelligence (AI) system for the detection of nasopharyngeal carcinoma (NPC) using …

Objectives/Hypothesis To develop a deep‐learning‐based automatic diagnosis system for
identifying nasopharyngeal carcinoma (NPC) from noncancer (inflammation and …

Background Due to the occult anatomic location of the nasopharynx and frequent presence
of adenoid hyperplasia, the positive rate for malignancy identification during biopsy is low …

Nasopharyngeal carcinoma (NPC) is known for high curability during early stage of the
disease, and early diagnosis relies on nasopharyngeal endoscopy and subsequent …

Laryngeal cancer poses a significant global health burden, with late-stage diagnoses
contributing to reduced survival rates. This study explores the application of deep …

Objective To develop a deep convolutional neural network (DCNN) that can automatically
detect laryngeal cancer (LCA) in laryngoscopic images. Methods A DCNN-based diagnostic …

Objectives A convolutional neural network (CNN) was adapted to automatically detect early-
stage nasopharyngeal carcinoma (NPC) and discriminate it from benign hyperplasia on a …

The pathologic diagnosis of nasopharyngeal carcinoma (NPC) by different pathologists is
often inefficient and inconsistent. We have therefore introduced a deep learning algorithm …

Objectives The prognosis for pharyngeal cancer is relatively poor. It is usually diagnosed in
an advanced stage. Although the recent development of narrow‐band imaging (NBI) and …

Pathologic diagnosis of nasopharyngeal carcinoma (NPC) can be challenging since most
cases are nonkeratinizing carcinoma with little differentiation and many admixed …