DGRNet: Disagreement-Guided Refinement for Uncertainty-Aware Brain Tumor Segmentation
DGRNet addresses two critical gaps in brain tumor segmentation: reliable uncertainty quantification and under-utilization of radiology reports. The core idea transforms prediction disagreement among multiple lightweight view-specific adapters into an active signal that guides targeted refinement in ambiguous regions, integrated with clinical text conditioning. This approach achieves state-of-the-art accuracy on the TextBraTS benchmark while providing clinically meaningful uncertainty estimates calibrated to actual errors.
The paper presents a technically sound and well-structured approach that successfully combines multi-view uncertainty estimation with selective text-guided refinement. The proposed diversity-preserving mechanisms effectively mitigate view collapse, and the quantitative improvements (2.4% Dice and 11% HD95) are substantiated through methodical ablation studies. The work represents a meaningful contribution to uncertainty-aware medical segmentation, though its impact is contingent on validation beyond the single TextBraTS dataset and clarification of computational costs.
The FiLM-based view-specific adapter design elegantly generates diverse predictions without requiring multiple full models or stochastic forward passes. The diversity-preserving training strategy—combining bias initialization, pairwise similarity penalties, and gradient isolation—directly addresses the critical failure mode of view collapse, with Table 4 empirically validating its necessity. The uncertainty quantification demonstrates strong clinical utility, achieving an Error Detection AUC of 0.910 and an Uncertainty Ratio of 239.4$\times$, indicating the model is substantially more uncertain about erroneous predictions.
The claim of computational efficiency via "single forward pass" is misleading because the model effectively computes four parallel view-specific adaptations, disagreement aggregation, and a full refinement module, substantially increasing FLOPs despite modest parameter growth. The text-conditioning relies on frozen BioBERT embeddings without domain-specific fine-tuning, potentially limiting semantic alignment with radiological descriptions. Additionally, the optimal choice of four views (Table 3) appears determined via post-hoc grid search rather than principled analysis, suggesting possible dataset-specific overfitting. The paper also omits statistical significance testing for the reported improvements.
The ablation studies in Table 2 provide compelling evidence for incremental component contributions, showing consistent improvements from multi-view prediction (85.6% Dice) through full refinement (87.6% Dice). However, the comparison in Table 1 raises concerns: the reproduced TextBraTS baseline (84.9% Avg. Dice) significantly underperforms the originally reported values (85.3%), suggesting potential implementation inconsistencies. The paper lacks statistical significance testing for the claimed 2.4% improvement, and comparisons are limited to a single dataset, constraining generalizability claims.
The paper provides partial implementation details including PyTorch/MONAI frameworks, SAM optimizer (lr=0.1, momentum=0.9), and 200 epochs with batch size 1. However, critical hyperparameters for the multi-objective loss ($\lambda_a, \lambda_c, \lambda_v$ in Equation 1) are unspecified, and the exact architecture dimensions (e.g., MLP hidden sizes for FiLM generators) remain unclear. No code or pre-trained models are indicated as publicly available, significantly hindering independent reproduction. While the TextBraTS dataset is public, the text preprocessing pipeline and attention pooling implementation details are insufficiently described.
Accurate brain tumor segmentation from MRI scans is critical for diagnosis and treatment planning. Despite the strong performance of recent deep learning approaches, two fundamental limitations remain: (1) the lack of reliable uncertainty quantification in single-model predictions, which is essential for clinical deployment because the level of uncertainty may impact treatment decision-making, and (2) the under-utilization of rich information in radiology reports that can guide segmentation in ambiguous regions. In this paper, we propose the Disagreement-Guided Refinement Network (DGRNet), a novel framework that addresses both limitations through multi-view disagreement-based uncertainty estimation and text-conditioned refinement. DGRNet generates diverse predictions via four lightweight view-specific adapters attached to a shared encoder-decoder, enabling efficient uncertainty quantification within a single forward pass. Afterward, we build disagreement maps to identify regions of high segmentation uncertainty, which are then selectively refined according to clinical reports. Moreover, we introduce a diversity-preserving training strategy that combines pairwise similarity penalties and gradient isolation to prevent view collapse. The experimental results on the TextBraTS dataset show that DGRNet favorably improves state-of-the-art segmentation accuracy by 2.4% and 11% in main metrics Dice and HD95, respectively, while providing meaningful uncertainty estimates.
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