According to the
endometrial cancer cell detection method and
system provided by the embodiment of the invention, firstly, an image block is paired with a
pathological report text, and unified semantic features are obtained by utilizing multi-
positive sample comparison learning; performing semantic entropy-driven dynamic shielding-random
shuffling self-supervision training on the visual neural network; secondly, after
cell nucleuses are positioned, a weighted
cell graph is constructed with cells as nodes and proximity relations as edges, topological features are obtained, and then the topological features, visual features and semantic features are subjected to cross-scale attention and gating fusion to generate multi-
modal features; a detection head jointly outputs a category, a frame and a
mask, and comprehensive cross-
modal consistency loss is subjected to supervision or weak supervision training; and a'
cancer-normal 'preference pair is automatically generated by further utilizing high and low probability regions output by the initial model, a reward model is trained, and a PPO strategy is adopted for iterative
fine tuning, so that cell-level
cancer focus detection can be accurately and robustly completed without a large number of pixel-level labels, and the
pathological screening efficiency and accuracy are remarkably improved.