Explainable Deep Survival Learning Reveals Tumor Spatial Features and Stromal Patterns for Recurrence-Free Survival Prediction In Neuroendocrine Tumor Liver Metastases
To develop an explainable deep learning framework for histological segmentation and prognostic modeling of neuroendocrine tumors (NET) liver metastasis, comparing the efficacy of non-linear deep survival analysis against traditional linear Cox regression.
Poster Program · Diagnostic and Interventional Radiology Physics