title: Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric
    Approach
summary: Automated interpretation of CT images-particularly localizing and describing
  abnormal findings across multi-plane and whole-body scans-remains a significant
  challenge in clinical radiology. This work aims to address this challenge
  through four key contributions: (i) On taxonomy, we collaborate with senior
  radiologists to propose a comprehensive hierarchical classification system,
  with 404 representative abnormal findings across all body regions; (ii) On
  data, we contribute a dataset containing over 14.5K CT images from multiple
  planes and all human body regions, and meticulously provide grounding
  annotations for over 19K abnormalities, each linked to the detailed description
  and cast into the taxonomy; (iii) On model development, we propose
  OminiAbnorm-CT, which can automatically ground and describe abnormal findings
  on multi-plane and whole-body CT images based on text queries, while also
  allowing flexible interaction through visual prompts; (iv) On benchmarks, we
  establish three representative evaluation tasks based on real clinical
  scenarios. Through extensive experiments, we show that OminiAbnorm-CT can
  significantly outperform existing methods on all the tasks and metrics.
paper_authors._id: 684158e2f11e4b2c51fce923
paper_authors.name: Ziheng Zhao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-05T10:00:00.086Z
paper_authors._id: 6496eae78a7c70379a512e39
paper_authors.avatarUrl: /avatars/f5ab483ae93cc04b43e825dfd9440905.svg
paper_authors.fullname: Ziheng Zhao
paper_authors.type: user
paper_authors.user: zzh99
paper_authors._id: 684158e2f11e4b2c51fce924
paper_authors.name: Lisong Dai
paper_authors._id: 684158e2f11e4b2c51fce925
paper_authors.name: Ya Zhang
paper_authors._id: 684158e2f11e4b2c51fce926
paper_authors.name: Yanfeng Wang
paper_authors._id: 684158e2f11e4b2c51fce927
paper_authors.name: Weidi Xie
paper_title: Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric
    Approach
paper_summary: Automated interpretation of CT images-particularly localizing and describing
  abnormal findings across multi-plane and whole-body scans-remains a significant
  challenge in clinical radiology. This work aims to address this challenge
  through four key contributions: (i) On taxonomy, we collaborate with senior
  radiologists to propose a comprehensive hierarchical classification system,
  with 404 representative abnormal findings across all body regions; (ii) On
  data, we contribute a dataset containing over 14.5K CT images from multiple
  planes and all human body regions, and meticulously provide grounding
  annotations for over 19K abnormalities, each linked to the detailed description
  and cast into the taxonomy; (iii) On model development, we propose
  OminiAbnorm-CT, which can automatically ground and describe abnormal findings
  on multi-plane and whole-body CT images based on text queries, while also
  allowing flexible interaction through visual prompts; (iv) On benchmarks, we
  establish three representative evaluation tasks based on real clinical
  scenarios. Through extensive experiments, we show that OminiAbnorm-CT can
  significantly outperform existing methods on all the tasks and metrics.
paper_ai_summary: OminiAbnorm-CT, a model for automated interpretation of CT images, outperforms existing methods in localizing and describing abnormalities across different body regions using text queries and visual prompts.
paper_ai_keywords: OminiAbnorm-CT