This is a multi-center, prospective, self-controlled, diagnostic accuracy comparative study of Artificial Intelligence Diagnostic System for Surgical Neuropathology. The investigators will compare the diagnostic efficiency of Artificial Intelligence with that of practicing pathologists, and suppose that the diagnostic efficiency of artificial intelligence in prospective clinical data is no less than that of pathologists.

In this study, 141 patients will be recruited. After being enrolled, the patients will accept surgery and specimens for pathological analysis will be taken according to the routine treatment process.

The histopathologic slides will then be digitized by a whole-slide scanner. The images will be reviewed by gold standard committee for evaluation of ground truth. And then be separately diagnosed by Artificial Intelligence Diagnostic System and practicing pathologists. So the investigators can compare the diagnostic efficiency of Artificial Intelligence with that of pathologists, thus understand the gap between artificial intelligence and actual clinical practice.

Inclusion Criteria:

1. Patients or their guardians understand the research process, agree to use their data, and sign the informed consent form;
2. Aged >=18 years;
3. MRI shows intracranial spaceoccupying lesions;
4. The clinical diagnosis is glioma, metastasis or lymphoma thus requiring surgical treatment;
5. The patient is willing to accept the surgery.

Exclusion Criteria:

1. The patient has serious underlying diseases thus is not suitable for surgery;
2. After further clinical evaluation, surgical treatment was not the best choice;
3. The patient participate in clinical research of other drugs or devices;
4. The researchers believe that there are other factors that will make the patients unable to complete the study.

The AI group, ordinary pathologists and gold standard group will not be informed of each other's results