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Unsupervised Segmentation of Colonoscopy Images

arXiv 2312.12599 ↗First posted 19 Dec 2023eess.IV cs.AI cs.CVcited by 0

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Abstract (arXiv)

Colonoscopy plays a crucial role in the diagnosis and prognosis of various gastrointestinal diseases. Due to the challenges of collecting large-scale high-quality ground truth annotations for colonoscopy images, and more generally medical images, we explore using self-supervised features from vision transformers in three challenging tasks for colonoscopy images. Our results indicate that image-level features learned from DINO models achieve image classification performance comparable to fully supervised models, and patch-level features contain rich semantic information for object detection. Furthermore, we demonstrate that self-supervised features combined with unsupervised segmentation can be used to discover multiple clinically relevant structures in a fully unsupervised manner, demonstrating the tremendous potential of applying these methods in medical image analysis.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 31 references that are in the map. First posted on arXiv 19 Dec 2023.

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19 of 31 references.

References · 19

  1. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  2. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  3. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  4. 2021Deep ViT Features as Dense Visual Descriptorscited by 330
  5. 2022Transformers in Medical Imaging: A Surveycited by 245
  6. 2022Unsupervised Semantic Segmentation by Distilling Feature Correspondencescited by 241
  7. 2021Unsupervised Semantic Segmentation by Contrasting Object Mask Proposalscited by 190
  8. 2022Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cutcited by 167
  9. 2022Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localizationcited by 150
  10. 2022Self-Supervised Learning of Object Parts for Semantic Segmentationcited by 90
  11. 2021Self-supervised learning methods and applications in medical imaging analysis: A surveycited by 77
  12. 2023Foundation Model for Endoscopy Video Analysis via Large-scale Self-supervised Pre-traincited by 75
7 more
  1. 2022Dissecting Self-Supervised Learning Methods for Surgical Computer Visioncited by 48
  2. 2022Texture-guided Saliency Distilling for Unsupervised Salient Object Detectioncited by 24
  3. 2021Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Imagescited by 23
  4. 2023Self-Supervised Learning for Endoscopic Video Analysiscited by 22
  5. 2019Coarse Graining of Data via Inhomogeneous Diffusion Condensationcited by 9
  6. 2020Motion-based Camera Localization System in Colonoscopy Videoscited by 9
  7. 2023Semantic Parsing of Colonoscopy Videos with Multi-Label Temporal Networkscited by 3

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