This invention relates to the field of computer-aided
diagnostic technology, and provides a method for constructing an intelligent
pathological auxiliary
diagnostic model for lipomas based on weakly
supervised learning. The method includes acquiring several whole-slice images of lipomas; preprocessing the whole-slice images; constructing a structured square image patch dataset suitable for weakly
supervised learning; classifying and storing the images according to their dataset and
pathological label categories in a
structured text index file; constructing a lightweight ViT model as the
core network architecture, serializing and embedding the square image patches, learning features through a multi-layer self-attention mechanism, and connecting them to a global
pooling classification head to form a weakly supervised
diagnostic model that relies on image-level labels for end-to-end training; and applying weakly
supervised training strategies and optimizations to the weakly supervised
diagnostic model to achieve an interpretable intelligent
diagnostic system for lipomas. This invention constructs an end-to-end solution suitable for weakly
supervised learning of
pathological images.