As Artificial Intelligence (AI) is rapidly gaining popularity, and it has shown impressive capabilities in medicine, even outperforming humans. The range of potential applications is already large and only growing by the day. AI has shown its power in ophthalmology, radiologic diagnoses, pharmacogenetics, gastroenterology, and many more fronts. AI has produced disruptive changes in medicine, achieving superior efficacy in many tasks by leveraging the large amount of information available in the big data era. AI also showed a considerable interest in the area of tumor data processing in recent years. Due to the spread of medical data, modalities such as Magnetic Resonance Imaging (MRI), Ultrasound, and Positron Emission Tomography (PET), dermoscopic images, x-ray images, mammograms, and histological images, massive data are being generated related to the tumor. The tumor data is generated in the form of some images and related to health informatics.

However, the tumor data is massive and difficult to use by employing classical techniques (i.e. hand-crafted features). Tumor data processing involves increasing information techniques such as data storage and analysis. The question is that how we can use this massive tumor data to build the automated system with better accuracy and less computational time. Moreover, how we can utilize this massive tumor data to develop an automated system for better diagnosis of tumors such as brain tumor, skin cancer, lung cancer, stomach cancer, and breast cancer. AI techniques are dominated by machine learning techniques capable of extracting patterns from massive data as well as building reasoning systems for tumor patient risk stratification and better decision making. For more accurate tumor patient-level predictions and modeling disease prognosis, AI techniques have consistently outperformed traditional statistical methods. Therefore, with the help of AI techniques, collecting and mining available information from massive tumor data, looking for internal connections and rules will bring unprecedented opportunities for tumor research and diagnosis.

This Special Issue aims to focus mainly on the design, architecture, and application of AI for data processing and diagnosis of tumors. This Special Issue welcomes original research and review articles discussing the challenges associated with this topic.

Potential topics include but are not limited to the following:

• AI techniques for feature extraction in tumor images
• Classification of tumors using AI techniques
• AI-based image-guided therapy for tumor detection
• AI techniques for tumor big data processing
• AI for tumor image retrieval
• AI for high accuracy computer-aided detection/diagnosis systems in tumors
• AI-driven tumor modeling and simulation using omics datasets
• AI for data mining in tumor theranostics
• AI for text analytics and natural language processing (NLP) in tumors
• Automatic semantic annotation of medical content in the context of tumor disease
• Internet of medical things using AI techniques for tumor cancer detection
• AI techniques for early tumor detection
• AI techniques for prediction of tumor location
• AI techniques for the development of anti-tumor drugs
• AI techniques for tumor prognosis prediction