Background <p>Estimating the postmortem interval (PMI) is a key task in forensic science. Deep learning-based pathology image analysis offers a promising approach, but existing pathomics methods face two major challenges: limited translatability from animal to human samples and insufficient model interpretability.</p> Methods <p>We propose a PMI estimation framework based on a pathomics foundation model with a two-stage cross-species transfer learning strategy. In the first stage, the model is fine-tuned on porcine liver whole-slide images (WSIs); in the second, it is further fine-tuned with a small amount of human data to achieve effective knowledge transfer. To improve interpretability, model predictions are visualized at the whole-slide level using probability maps, class maps, and classification proportion histograms.</p> Results <p>Sixteen porcine and twenty-three human samples were used to evaluate four deep learning models—ResNet50, DenseNet121, SongCi, and UNI—for PMI estimation. The Vision Transformer-based UNI model achieved the best performance, with 91.63% accuracy in porcine data. After transfer learning with limited human samples, accuracy increased to 78.95%, representing a more than 50% improvement compared to the untuned model. The visualization framework further enhanced interpretability and traceability of the model’s outputs.</p> Conclusion <p>This study demonstrates that combining animal data priors with a fine-tuning strategy using minimal human data and whole-slide visualization enables cross-species PMI estimation. The proposed framework addresses data scarcity, enhances model transparency, and provides a practical and interpretable AI-based tool for forensic pathology.</p>