The invention discloses a
medical image diagnosis auxiliary system based on
deep learning, and relates to the technical field of medical
image processing. According to the method, through integrity
verification, exclusive
noise suppression and space
standardization processing, dynamic changes of focus characteristics are captured through a
time sequence attention mechanism, meanwhile, a medical
pathology professional
knowledge graph is called to achieve accurate matching of the
time sequence focus characteristics, an anatomical structure and
pathology type nodes, and the risk of
missed diagnosis and misdiagnosis is greatly reduced; the confidence coefficient of a diagnosis assistance conclusion is evaluated through a multi-dimensional factor, manual review is automatically prompted for a low-confidence-coefficient result, dual management and control of
system assistance and manual check are formed, meanwhile, standardized focus positioning,
pathological type candidate and diagnosis basis can be provided for basic-level or inexperienced doctors, and the diagnosis accuracy is improved. Repeated work such as data arrangement can be reduced for experienced doctors, the doctors are assisted to focus on complex case judgment, the diagnosis and
treatment level of a
medical team is integrally improved, medical risks are effectively reduced, and the diagnosis and
treatment quality and efficiency are improved.