{
  "annotation_id": "6fb6c2a8-8b9e-4a00-8526-e6589237347c",
  "date_created": "2026-02-17T05:53:20.007000Z",
  "date_modified": "2026-02-17T05:53:20.007000Z",
  "file_hash": "5b3d3c8b948f3c19fca5e39d42ffd456c6f34e6b929e38de29b97ef1221f8a35",
  "private": false,
  "record": {
    "abstract": "Brain tumor segmentation is essential for diagnosis and treatment planning, yet many CNN and U-Net based approaches produce noisy boundaries in regions of tumor infiltration. We introduce UAMSA-UNet, an Uncertainty-Aware Multi-Scale Attention-based Bayesian U-Net that in- stead leverages Monte Carlo Dropout to learn a data-driven smoothing prior over its predictions, while fusing multi-scale features and attention maps to capture both fine details and global context. Our smoothing-regularized loss augments binary cross-entropy with a variance penalty across stochas- tic forward passes, discouraging spurious fluctuations and yielding spatially coherent masks. On BraTS2023, UAMSA- UNet improves Dice Similarity Coefficient by up to 3.3% and mean IoU by up to 2.7% over U-Net; on BraTS2024, it delivers up to 4.5% Dice and 4.0% IoU gains over the best baseline. Remarkably, it also reduces FLOPs by 42.5% rel- ative to U-Net++ while maintaining higher accuracy. These results demonstrate that, by combining multi-scale attention with a learned smoothing prior, UAMSA-UNet achieves both better segmentation quality and computational efficiency, and provides a flexible foundation for future integration with transformer-based modules for further enhanced segmenta- tion results.",
    "arxiv_id": "2601.08956",
    "authors": [
      "Satyaki Roy Chowdhury",
      "Golrokh Mirzaei"
    ],
    "categories": [
      "cs.CV"
    ],
    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "title": "Variance-Penalized MC-Dropout as a Learned Smoothing Prior for Brain Tumour Segmentation",
    "url": "https://arxiv.org/abs/2601.08956",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "a51ab7fe-109b-41f3-8569-f5f7ea04d9cb",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}