A structure-aware and causal-invariant framework for glioma cell classification in pathological images (opens in new tab)

IntroductionAccurate classification of glioma cells and normal cells remains challenging due to insufficient fine-grained pathological feature representation, limited modeling of morphological relationships, and interference from staining variation, background noise, and imaging bias.MethodsThis study proposes a hybrid classification framework that integrates a convolutional residual local detail enhancement front-end, a Transformer backbone, a morphological relation modeling mechanism, and a...