mlsanityData to 7 Oct 2026

Frontiers in Intelligent Colonoscopy

arXiv 2410.17241 ↗First posted 22 Oct 2024eess.IV cs.CVcited by 13

Not on the map. Why?

Abstract (arXiv)

Colonoscopy is currently one of the most sensitive screening methods for colorectal cancer. This study investigates the frontiers of intelligent colonoscopy techniques and their prospective implications for multimodal medical applications. With this goal, we begin by assessing the current data-centric and model-centric landscapes through four tasks for colonoscopic scene perception, including classification, detection, segmentation, and vision-language understanding. This assessment enables us to identify domain-specific challenges and reveals that multimodal research in colonoscopy remains open for further exploration. To embrace the coming multimodal era, we establish three foundational initiatives: a large-scale multimodal instruction tuning dataset ColonINST, a colonoscopy-designed multimodal language model ColonGPT, and a multimodal benchmark. To facilitate ongoing monitoring of this rapidly evolving field, we provide a public website for the latest updates: https://github.com/ai4colonoscopy/IntelliScope.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 166 of its 312 references that are in the map. First posted on arXiv 22 Oct 2024.

Builds on

166 of 312 references.

References · 166

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  5. 2023GPT-4 Technical Reportcited by 18,132
  6. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  7. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  8. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  9. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  10. 2023Segment Anythingcited by 8,158
  11. 2023Visual Instruction Tuningcited by 7,792
  12. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
38 more
  1. 2020End-to-End Object Detection with Transformerscited by 7,384
  2. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  3. 2016Densely Connected Convolutional Networkscited by 6,691
  4. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  5. 2020Scaling Laws for Neural Language Modelscited by 6,411
  6. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,069
  7. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  8. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  9. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  10. 2023Improved Baselines with Visual Instruction Tuningcited by 4,362
  11. 2022A ConvNet for the 2020scited by 3,545
  12. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
  13. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  14. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  15. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  16. 2023Sigmoid Loss for Language Image Pre-Trainingcited by 3,048
  17. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  18. 2024SAM 2: Segment Anything in Images and Videoscited by 2,784
  19. 2018YOLOv3: An Incremental Improvementcited by 2,652
  20. 2023InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Taskscited by 2,534
  21. 2018CBAM: Convolutional Block Attention Modulecited by 2,463
  22. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  23. 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,338
  24. 2016Identity Mappings in Deep Residual Networkscited by 1,960
  25. 2022Large Language Models Encode Clinical Knowledgecited by 1,807
  26. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
  27. 2019Deep High-Resolution Representation Learning for Visual Recognitioncited by 1,417
  28. 2023LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Daycited by 1,287
  29. 2021Grounded Language-Image Pre-trainingcited by 1,147
  30. 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Modelcited by 1,051
  31. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,018
  32. 2024Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Predictioncited by 1,005
  33. 2024VMamba: Visual State Space Modelcited by 955
  34. 2021CvT: Introducing Convolutions to Vision Transformerscited by 946
  35. 2023RWKV: Reinventing RNNs for the Transformer Eracited by 814
  36. 2021PVT v2: Improved Baselines with Pyramid Vision Transformercited by 749
  37. 2022Galactica: A Large Language Model for Sciencecited by 736
  38. 2023ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoderscited by 703

50 most cited of 166 All 166 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 8; 2026: 3.

13 papers.

Sort:

By area:

  1. 2025EndoBench: A Comprehensive Evaluation of Multi-Modal Large Language Models for Endoscopy Analysiscited by 13
  2. 2025Deep Learning in Concealed Dense Predictioncited by 6
  3. 2025PraNet-V2: Dual-Supervised Reverse Attention for Medical Image Segmentationcited by 4
  4. 2025EndoCogniAgent: Closed-Loop Agentic Reasoning with Self-Consistency Validation for Endoscopic Diagnosiscited by 3
  5. 2024First-frame Supervised Video Polyp Segmentation via Propagative and Semantic Dual-teacher Networkcited by 1
  6. 2025LawDIS: Language-Window-based Controllable Dichotomous Image Segmentationcited by 1
  7. 2025GAPNet: A Lightweight Framework for Image and Video Salient Object Detection via Granularity-Aware Paradigmcited by 1
  8. 2024Optimizing Gastrointestinal Diagnostics: A CNN-Based Model for VCE Image Classificationcited by 0
  9. 2025FreeVPS: Repurposing Training-Free SAM2 for Generalizable Video Polyp Segmentationcited by 0
  10. 2025Colon-X: Advancing Intelligent Colonoscopy toward Clinical Reasoningcited by 0
  11. 2026Analogical Reasoning as a Doctor: A Foundation Model for Gastrointestinal Endoscopy Diagnosiscited by 0
  12. 2026AusSmoke meets MultiNatSmoke: a fully-labelled diverse smoke segmentation datasetcited by 0
1 more
  1. 2026FlowDIS: Language-Guided Dichotomous Image Segmentation with Flow Matchingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Colon-Bench: An Agentic Workflow for Scalable Dense Lesion Annotation in Full-Procedure Colonoscopy Videos
  2. 2025LDP: Parameter-Efficient Fine-Tuning of Multimodal LLM for Medical Report Generation
  3. 2025Vision Language Models versus Machine Learning Models Performance on Polyp Detection and Classification in Colonoscopy Images
  4. 2026A report-grounded vision-language foundation model for colonoscopy from 280000 routine reports
  5. 2026OpenRC: An Open-Source Robotic Colonoscopy Framework for Multimodal Data Acquisition and Autonomy Research ↗
  6. 2026GI-Bench: A Panoramic Benchmark Revealing the Knowledge-Experience Dissociation of Multimodal Large Language Models in Gastrointestinal Endoscopy Against Clinical Standards
  7. 2024CCIS-Diff: A Generative Model with Stable Diffusion Prior for Controlled Colonoscopy Image Synthesis
  8. 2026Performance of large language models in the optical diagnosis of colorectal polyps