SENCA-st: Integrating Spatial Transcriptomics and Histopathology with Cross Attention Shared Encoder for Region Identification in Cancer Pathology

SENCA-st represents a breakthrough in the integration of spatial transcriptomics and histopathology, two critical fields in cancer research. By addressing the shortcomings of existing segmentation methods, which often lose essential functional information or become overly generalized, SENCA-st preserves the unique features of both modalities. This is particularly important in identifying tumor substructures associated with cancer drug resistance, a key area of research that could lead to more effective treatments. The model's superior performance over current state-of-the-art methods underscores its potential impact on cancer pathology, paving the way for more precise and informative analyses that could ultimately improve patient outcomes.