# ROBUST-WT: Robust Uncertainty-aware Segmentation Transform via Whitening and Training Enhancements Source: [ROBUST-WT: Robust Uncertainty-aware Segmentation Transform via Whitening and Training Enhancements]() Collection index: [llms.txt]() Data updated: 2026-06-04T05:00:05.280Z Authors: Aqsa Naseer, Maryam Bibi, Syeda Samiya Urooj, Muhammad Khurram Shahzad Published: 2026-06-02T02:59:35.000Z ## Abstract Generalized segmentation of medical images prevents performance degradation when different imaging devices and clinical protocols are used across multiple domains. The Whitening Transform-based Probabilistic Shape Regularization Extractor (WT-PSE), published in IEEE Transactions on Medical Imaging in 2024, addresses this challenge by employing feature decorrelation and Wasserstein distance-based knowledge distillation to achieve robust cross-domain segmentation. This study systematically examines improvements to the WT-PSE learning framework. Four limitations in the original implementation are identified: limited training augmentations that fail to simulate real scanner variations, reliance on per-pixel binary cross-entropy loss that is sensitive to edge noise, the absence of a scheduled loss weighting strategy that may destabilize early training, and the lack of ablation switches for controlled scientific comparison. To address these issues, we propose four enhancements: (1) domain-adaptive augmentation including random erasing, gamma correction, and salt-and-pepper noise; (2) a hybrid BCE and Dice loss function for improved edge-aware segmentation under noisy conditions; (3) a curriculum-based Dice weight scheduling strategy; and (4) command-line control flags for systematic ablation studies. Experiments on the fundus optic disc segmentation benchmark demonstrate that the improved pipeline achieves a final epoch optic-disc Dice score of 0.956 and an ASD score of 13.31, outperforming the baseline epoch-5 Dice score of 0.939. These results indicate that training-level improvements can provide consistent performance gains without modifying the underlying WT-PSE architecture. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Optic Cup Segmentation on Fundus Image (held-out domain)](): Optic Cup Segmentation; Fundus Image (held-out domain); Dice: 84.42. - [Optic Disc Segmentation on Fundus Image (held-out target domain)](): Optic Disc Segmentation; Fundus Image (held-out target domain); Dice Coefficient: 95.66. ## Original sources and implementation [Original paper]() [PDF]()