fig2

Figure 2. The overall architecture of the proposed IF-SPECT model. (A) The 3D U-Net backbone used for extracting multi-scale features (F1, F2, F3) and generating the segmentation mask. (B) The Structure-Aware Module: This module enhances feature representation using upsampled masks and a Laplace kernel, aggregating features via Spatial Attention (SA) and Channel Attention (CA) modules. (C) Detailed structure of the Spatial Attention Module (top) and Channel Attention Module (bottom). (D) Final prognostic modeling: the DL-based risk score is combined with clinical characteristics (age, BMI, sex, NYHA_3_4) in a multivariable Cox model to generate the final IF-SPECT score.