mlsanityData to 7 Oct 2026

CoDiR: Confidence-Guided Diffusion Refinement for Semi-Supervised Histopathology Segmentation

arXiv 2608.11807 ↗First posted 12 Aug 2026cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Semi-supervised histopathology segmentation is challenging due to scarce annotations and unreliable pseudo-labels in ambiguous gland regions. To address this problem, we propose Confidence-Guided Diffusion Refinement (CoDiR), a semi-supervised framework that combines a Mean Teacher segmentation model with diffusion-based pseudo-label refinement. Given an unlabeled image, the teacher first produces a soft prediction, and only low-confidence regions are refined by a conditional diffusion model trained to capture plausible mask structures from labeled data. The refined mask is then fused with reliable teacher predictions and used to train the student with confidence weighting and consistency regularization. On the GlaS and CRAG datasets CoDiR reaches 88.09% and 89.83% mDice with 10% labeled data, and 89.19% and 90.29% mDice with 20%, matching or exceeding the strongest published method on seven of the eight benchmark metrics. Ablations attribute the largest single contribution to the refinement module, which adds +6.36% mDice over the Mean Teacher baseline. The implementation code is publicly available at: https://github.com/vongla345/codir

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 22 of its 29 references that are in the map. First posted on arXiv 12 Aug 2026.

Builds on

22 of 29 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024DiffRect: Latent Diffusion Label Rectification for Semi-supervised Medical Image Segmentation13 Jul

Other references · 21

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  4. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,154
  5. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
  6. 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,903
  7. 2019Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learningcited by 507
  8. 2021Semi-Supervised Semantic Segmentation with Cross Pseudo Supervisioncited by 462
  9. 2019Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentationcited by 378
  10. 2020clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentationcited by 286
  11. 2016Gland Segmentation in Colon Histology Images: The GlaS Challenge Contestcited by 203
  12. 2018Boundary loss for highly unbalanced segmentationcited by 187
9 more
  1. 2021Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformercited by 99
  2. 2018MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Imagescited by 87
  3. 2023CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentationcited by 43
  4. 2023SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathologycited by 25
  5. 2023Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentationcited by 24
  6. 2024DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image Segmentationcited by 9
  7. 2024XNet v2: Fewer Limitations, Better Results and Greater Universalitycited by 5
  8. 2025Learning Disentangled Stain and Structural Representations for Semi-Supervised Histopathology Segmentationcited by 4
  9. 2026UniSemAlign: Text-Prototype Alignment with a Foundation Encoder for Semi-Supervised Histopathology Segmentationcited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Semi-MoE: Mixture-of-Experts meets Semi-Supervised Histopathology Segmentation
  2. 2024Style-Extracting Diffusion Models for Semi-Supervised Histopathology Segmentation
  3. 2025PathSegDiff: Pathology Segmentation using Diffusion model representations
  4. 2025Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training
  5. 2023Realistic Data Enrichment for Robust Image Segmentation in Histopathology
  6. 2025Semantic and Visual Crop-Guided Diffusion Models for Heterogeneous Tissue Synthesis in Histopathology
  7. 2025PathDiff: Histopathology Image Synthesis with Unpaired Text and Mask Conditions
  8. 2023Diffusion-based Data Augmentation for Nuclei Image Segmentation