Advanced Deep Learning for Precise Multi-Organ Functional Tissue Segmentation

This study aims to advance medical research by identifying and segmenting functional tissue units (FTUs) within five human organs using deep learning techniques. The dataset comprises tissue slice images from the Human Protein Atlas (HPA) and the Human BioMolecular Atlas Program (HuBMAP). We assess segmentation accuracy using the mean Dice coefficient. Analysis indicates significant distributional differences across gender and age groups, prompting the design of varied sample weighting coefficients and sampling and embedding strategies. The proposed model, SegF x 2 + EffB7-B8 + FPN + ASPP, demonstrated superior performance compared to other models. This paper discusses data preprocessing, model definition, and integration, showcasing how our approach surpasses existing methods.