<p>Existing medical polyp segmentation networks predominantly rely on hierarchical feature representations to improve boundary delineation. However, we identify a critical yet underexplored issue, termed shallow information loss, wherein low-level layers irreversibly suppress low-contrast but semantically essential edge cues during forward propagation, while attenuated gradients in backpropagation are insufficient to recover such early-stage information loss. This problem is especially severe in polyps exhibiting fractal-like structural characteristics, where cross-scale self-similarity is progressively disrupted across spatial resolutions, ultimately degrading segmentation performance. Moreover, most existing approaches attempt to learn a direct pixel-to-semantic mapping in a single step, lacking progressive feature guidance and resulting in inadequate global semantic awareness. To address these limitations, we propose RDNet, a retrospective detail network composed of a backward detail reconstruction (BDR) module and a cascaded synergistic optimization (CSO) module. The BDR module injects discriminative semantic information backward in a layer-wise manner to recover weak-response regions that are suppressed during forward propagation, while cross-layer feature calibration enables accurate reactivation of shallow structural details. The CSO module further decouples boundary and region representations and employs a conditional gating mechanism to selectively activate complementary features, thereby enhancing structural consistency and semantic discrimination. Extensive experiments conducted on five public benchmark datasets demonstrate that RDNet consistently outperforms state-of-the-art methods, with particularly significant improvements in fractal-like multi-polyp segmentation scenarios, validating its robustness and effectiveness in complex clinical environments.</p>