title: Frontiers in Intelligent Colonoscopy
summary: 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.
paper_authors._id: 67188d1ce4c594992b657d03
paper_authors.name: Ge-Peng Ji
paper_authors._id: 67188d1ce4c594992b657d04
paper_authors.name: Jingyi Liu
paper_authors._id: 67188d1ce4c594992b657d05
paper_authors.name: Peng Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-24T08:37:11.566Z
paper_authors._id: 66a1d926da490fbd69091715
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/Z5AFfJxktltm_45EAN6U3.jpeg
paper_authors.fullname: pengxu
paper_authors.type: user
paper_authors.user: pengxu2024
paper_authors._id: 67188d1ce4c594992b657d06
paper_authors.name: Nick Barnes
paper_authors._id: 67188d1ce4c594992b657d07
paper_authors.name: Fahad Shahbaz Khan
paper_authors._id: 67188d1ce4c594992b657d08
paper_authors.name: Salman Khan
paper_authors._id: 67188d1ce4c594992b657d09
paper_authors.name: Deng-Ping Fan
paper_title: Frontiers in Intelligent Colonoscopy
paper_summary: 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.
paper_ai_summary: Intelligent colonoscopy techniques are explored through classification, detection, segmentation, and vision-language understanding tasks, addressing domain-specific challenges with the development of ColonINST, ColonGPT, and a multimodal benchmark.
paper_ai_keywords: data-centric model-centric colonoscopic scene perception classification detection segmentation vision-language understanding multimodal instruction tuning multimodal language model multimodal benchmark