DenseTRF: Texture-Aware Unsupervised Representation Adaptation for Surgical Scene Dense Prediction

• URL: http://arxiv.org/abs/2605.11265v1
• Date: Mon, 11 May 2026 21:43:40 GMT
• Title: DenseTRF: Texture-Aware Unsupervised Representation Adaptation for Surgical Scene Dense Prediction
• Abstract summary: We propose DenseTRF, a self-supervised representation adaptation framework based on texture-centric attention.<n>By adapting representations to the target distribution without supervision, DenseTRF significantly improves robustness to domain shifts.<n> Experiments across multiple surgical procedures demonstrate improved cross-distribution generalization in comparison to state-of-the-art segmentation models.
• Score: 0.609006146869448
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Dense prediction tasks in surgical computer vision, such as segmentation and surgical zone prediction, can provide valuable guidance for laparoscopic and robotic surgery. However, these models often suffer from distribution shifts, as training datasets rarely cover the variability encountered during deployment, leading to poor generalization. We propose DenseTRF, a self-supervised representation adaptation framework based on texture-centric attention. Our method leverages slot attention to learn texture-aware representations that capture invariant visual structures. By adapting these representations to the target distribution without supervision, DenseTRF significantly improves robustness to domain shifts. The framework is implemented through conditioning dense prediction on slot attention and model merging strategies. Experiments across multiple surgical procedures demonstrate improved cross-distribution generalization in comparison to state-of-the-art segmentation models and test-distribution adaptation methods for dense prediction tasks.

Related papers

• CheXanatomy: Anatomy-Aware Vision-Language Modeling for Chest Radiographs [3.369056928100267]
  CheXanatomy is a framework that integrates explicit anatomical knowledge into a pretrained vision-language model.<n>To enable scalable supervision, we synthesize realistic chest radiographs from CT volumes and forward-project CT segmentation labels.<n>We evaluate the approach on synthetic and real chest radiographs against a U-Net baseline, including ablations on model scale, input resolution, and vision encoder fine-tuning.
  arXiv  Detail & Related papers  (2026-06-07T02:41:05Z)
• Did Models Sufficient Learn? Attribution-Guided Training via Subset-Selected Counterfactual Augmentation [61.248535801314375]
  Subset-Selected Counterfactual Augmentation (SS-CA)<n>We develop Counterfactual LIMA to identify minimal spatial region sets whose removal can selectively alter model predictions.<n>Experiments show that SS-CA improves generalization on in-distribution (ID) test data and achieves superior performance on out-of-distribution (OOD) benchmarks.
  arXiv  Detail & Related papers  (2025-11-15T08:39:22Z)
• Resource-efficient Automatic Refinement of Segmentations via Weak Supervision from Light Feedback [1.8082075562656847]
  We present SCORE, a weakly supervised framework that learns to refine mask predictions only using light feedback during training.<n>We demonstrate SCORE on humerus CT scans, where it considerably improves initial predictions and achieves performance on par with existing refinement methods.
  arXiv  Detail & Related papers  (2025-11-04T13:53:10Z)
• UniSegDiff: Boosting Unified Lesion Segmentation via a Staged Diffusion Model [53.34835793648352]
  We propose UniSegDiff, a novel diffusion model framework for lesion segmentation.<n>UniSegDiff addresses lesion segmentation in a unified manner across multiple modalities and organs.<n> Comprehensive experimental results demonstrate that UniSegDiff significantly outperforms previous state-of-the-art (SOTA) approaches.
  arXiv  Detail & Related papers  (2025-07-24T12:33:10Z)
• UWarp: A Whole Slide Image Registration Pipeline to Characterize Scanner-Induced Local Domain Shift [0.8636895084411372]
  We present a domain shift analysis framework based on UWarp, a novel registration tool to align histological slides scanned under varying conditions.<n>Experiments demonstrate that UWarp outperforms existing open-source registration methods, achieving a median target registration error (TRE) of less than 4 pixels.<n>We apply UWarp to characterize scanner-induced local domain shift in the predictions of Breast-NEOprAIdict, a deep learning model for breast cancer pathological response prediction.
  arXiv  Detail & Related papers  (2025-03-26T15:48:38Z)
• Generalized Face Forgery Detection via Adaptive Learning for Pre-trained Vision Transformer [54.32283739486781]
  We present a textbfForgery-aware textbfAdaptive textbfVision textbfTransformer (FA-ViT) under the adaptive learning paradigm. FA-ViT achieves 93.83% and 78.32% AUC scores on Celeb-DF and DFDC datasets in the cross-dataset evaluation.
  arXiv  Detail & Related papers  (2023-09-20T06:51:11Z)
• Consistency Regularization for Generalizable Source-free Domain Adaptation [62.654883736925456]
  Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset. Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets. We propose a consistency regularization framework to develop a more generalizable SFDA method.
  arXiv  Detail & Related papers  (2023-08-03T07:45:53Z)
• Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective [51.70661197256033]
  We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation. We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks. We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
  arXiv  Detail & Related papers  (2023-02-03T13:50:25Z)
• Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classification [5.6512908295414]
  We propose an unsupervised domain adaptation approach that uses graph neural networks and, disentangled semantic and domain invariant structural features. We test the proposed method for classification on two challenging medical image datasets with distribution shifts. Experiments show our method achieves state-of-the-art results compared to other domain adaptation methods.
  arXiv  Detail & Related papers  (2022-06-27T09:02:16Z)
• Consistent Posterior Distributions under Vessel-Mixing: A Regularization for Cross-Domain Retinal Artery/Vein Classification [30.30848090813239]
  We propose a vessel-mixing based consistency regularization framework, for cross-domain learning in retinal A/V classification. Our method achieves the state-of-the-art cross-domain performance, which is also close to the upper bound obtained by fully supervised learning on target domain.
  arXiv  Detail & Related papers  (2021-03-16T14:18:35Z)
• Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting [86.33696045574692]
  We propose a Cycle Consistency Panoptic Domain Adaptive Mask R-CNN (CyC-PDAM) architecture for unsupervised nuclei segmentation in histopathology images. We first propose a nuclei inpainting mechanism to remove the auxiliary generated objects in the synthesized images. Secondly, a semantic branch with a domain discriminator is designed to achieve panoptic-level domain adaptation.
  arXiv  Detail & Related papers  (2020-05-05T11:08:26Z)
• Towards Better Surgical Instrument Segmentation in Endoscopic Vision: Multi-Angle Feature Aggregation and Contour Supervision [22.253074722129053]
  We propose a general embeddable approach to improve current deep neural network (DNN) segmentation models. The proposed method is validated with ablation experiments on the novel Sinus-Surgery datasets collected from surgeons' operations.
  arXiv  Detail & Related papers  (2020-02-25T05:28:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.