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

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

Background Laryngeal squamous cell carcinoma (LSCC) is one of the most common tumors
of the respiratory tract. Currently, the diagnosis of LSCC is mainly based on a laryngoscopy …

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

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 …

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

Nasopharyngeal carcinoma (NPC) is one of the most common head and neck cancers. Early
diagnosis plays a critical role in the treatment of NPC. To aid diagnosis, deep learning …

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

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 …

Background Laryngopharyngeal cancer (LPC) includes laryngeal and hypopharyngeal
cancer, whose early diagnosis can significantly improve the prognosis and quality of life of …