This application relates to the field of
medical information technology and discloses an AI-based automatic diagnosis
system for case consultation and
pathological slides. The
system includes a
server and a
client. The
server uses a
deep learning model to generate a binary
mask of lesions, constructs a sparse
quadtree index carrying a composite state bitmask, and generates vector slices with background removed. The
client distributes resource requests to multi-level queues based on the bitmask in the index, prioritizing the loading of high-value region images. A three-layer heterogeneous rendering architecture is adopted, using adaptive
Gaussian blur to process the vector slices for smooth transitions. Simultaneously, a behavior auditing module completes the state acquisition
viewport trajectory based on physical rendering, calculates the coverage rate of high-risk areas, and controls the submission permissions of diagnostic reports. This invention effectively reduces the
transmission bandwidth of gigapixel-level slices, improves loading
flicker, and reduces the risk of missed diagnoses due to network latency or
human error.