Yongbo Shu

Yongbo Shu, Wenzhao Xie, Shanhu Yao et al.

Multi-parametric prostate MRI combines T2-weighted (T2W), apparent diffusion coefficient (ADC), and high b-value diffusion-weighted (HBV) sequences for non-invasive detection of clinically significant prostate cancer. In practice, the diffusion sequences are more frequently subject to acquisition variability, motion, and artifacts than T2W, making robust fusion of these channels the clinically relevant requirement. We propose Modality-Isolated Gated Fusion (MIGF), an architecture-agnostic module that maintains separate modality-specific encoding streams before a learned gating stage, combined with modality dropout training to enforce compensation under incomplete inputs. We benchmark six backbones and assess MIGF-equipped models under seven missing-modality and artifact scenarios on PI-CAI (1,500 studies, fold-0 split, five seeds). MIGF improved ideal-scenario Ranking Score for UNet, nnUNet, and Mamba by 2.8%, 4.6%, and 13.4%; the best model (MIGFNet-nnUNet, gating + ModDrop, no deep supervision) achieved 0.7304 +/- 0.056. MIGF primarily improved tolerance to HBV/ADC degradation, while missing T2W remained a shared failure mode across all architectures. Mechanistic analysis shows that robustness gains arise from strict modality isolation and dropout-driven compensation rather than adaptive per-sample quality routing: the gate converged to a stable modality prior, and deep supervision was beneficial only for the largest backbone. External evaluation on Prostate158 (n=158) revealed substantial domain shift driven primarily by ADC map incompatibility across institutions; modality-isolation analysis confirmed that removing ADC improved external performance, identifying computed diffusion maps as inherently less portable than raw-acquired sequences.

Yongbo Shu, Kewen Chen, Yifeng Yuan et al.

Objectives: To characterize residual false positives in prostate MRI detection, and to evaluate a lightweight post-hoc refinement head for case-level specificity. Materials and Methods: This retrospective study used PI-CAI (5-fold cross-validation) and Prostate158 (n=158; external). A context-aware evidence head and an 89,216-parameter refinement head were trained on a frozen detection backbone; the evidence head was also trained on four further backbones (bare nnU-Net, bare U-Net, bare Mamba, MIGF-Mamba). For each false-positive region, T2-weighted, apparent-diffusion-coefficient, and high-b-value contrast ratios versus peri-lesional rings were compared against ground-truth lesions and contralateral benign regions. Results: False positives were closer to true cancers than to benign tissue in evidence and raw T2-weighted and apparent-diffusion-coefficient contrast, reproducing 35/35 across five architectures (Cohen's d 1.10; FP/benign evidence ratio 2.38x) and 105/105 across modality-perturbation scenarios. On PI-CAI fold-0, refinement raised case-level specificity from 0.469 to 0.549 (+17.2%) at preserved sensitivity (0.943); 5-fold cross-validation showed fold-conditional behavior (9/15 observations positive; range -22% to +28%). On Prostate158, both models saturated (McNemar pooled p=0.69), while the false-positive contrast-matching finding replicated. Conclusion: Residual false positives are contrast-matched to cancer (sharing raw imaging features rather than histologically confirmed mimicry), reproducing across five architectures -- a data-level imaging property, not model-specific artifacts; post-hoc refinement adds practical specificity in-domain but is fold-conditional.