title: ORID: Organ-Regional Information Driven Framework for Radiology Report
    Generation
summary: The objective of Radiology Report Generation (RRG) is to automatically
  generate coherent textual analyses of diseases based on radiological images,
  thereby alleviating the workload of radiologists. Current AI-based methods for
  RRG primarily focus on modifications to the encoder-decoder model architecture.
  To advance these approaches, this paper introduces an Organ-Regional
  Information Driven (ORID) framework which can effectively integrate multi-modal
  information and reduce the influence of noise from unrelated organs.
  Specifically, based on the LLaVA-Med, we first construct an RRG-related
  instruction dataset to improve organ-regional diagnosis description ability and
  get the LLaVA-Med-RRG. After that, we propose an organ-based cross-modal fusion
  module to effectively combine the information from the organ-regional diagnosis
  description and radiology image. To further reduce the influence of noise from
  unrelated organs on the radiology report generation, we introduce an organ
  importance coefficient analysis module, which leverages Graph Neural Network
  (GNN) to examine the interconnections of the cross-modal information of each
  organ region. Extensive experiments an1d comparisons with state-of-the-art
  methods across various evaluation metrics demonstrate the superior performance
  of our proposed method.
paper_authors._id: 673ee6ae8322f5b4acdcb5d4
paper_authors.name: Tiancheng Gu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-25T08:36:41.553Z
paper_authors._id: 641030c77a15af878ae5bd8f
paper_authors.avatarUrl: /avatars/8a5037edf55c78ebc317c8b191343671.svg
paper_authors.fullname: TianchengGu
paper_authors.type: user
paper_authors.user: TianchengGu
paper_authors._id: 673ee6ae8322f5b4acdcb5d5
paper_authors.name: Kaicheng Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-21T08:09:25.357Z
paper_authors._id: 63e202f352b7578dba448ab5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63e202f352b7578dba448ab5/8itVBLcv14m7OVsoF8h1o.jpeg
paper_authors.fullname: Yang
paper_authors.type: user
paper_authors.user: Kaichengalex
paper_authors._id: 673ee6ae8322f5b4acdcb5d6
paper_authors.name: Xiang An
paper_authors._id: 673ee6ae8322f5b4acdcb5d7
paper_authors.name: Ziyong Feng
paper_authors._id: 673ee6ae8322f5b4acdcb5d8
paper_authors.name: Dongnan Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-22T13:23:16.931Z
paper_authors._id: 65e7f19e14856e8859fd8adc
paper_authors.avatarUrl: /avatars/203e919c361db94028a1b3c6ea52f0c2.svg
paper_authors.fullname: Dongnan Liu
paper_authors.type: user
paper_authors.user: Nina0607
paper_authors._id: 673ee6ae8322f5b4acdcb5d9
paper_authors.name: Weidong Cai
paper_title: ORID: Organ-Regional Information Driven Framework for Radiology Report
    Generation
paper_summary: The objective of Radiology Report Generation (RRG) is to automatically
  generate coherent textual analyses of diseases based on radiological images,
  thereby alleviating the workload of radiologists. Current AI-based methods for
  RRG primarily focus on modifications to the encoder-decoder model architecture.
  To advance these approaches, this paper introduces an Organ-Regional
  Information Driven (ORID) framework which can effectively integrate multi-modal
  information and reduce the influence of noise from unrelated organs.
  Specifically, based on the LLaVA-Med, we first construct an RRG-related
  instruction dataset to improve organ-regional diagnosis description ability and
  get the LLaVA-Med-RRG. After that, we propose an organ-based cross-modal fusion
  module to effectively combine the information from the organ-regional diagnosis
  description and radiology image. To further reduce the influence of noise from
  unrelated organs on the radiology report generation, we introduce an organ
  importance coefficient analysis module, which leverages Graph Neural Network
  (GNN) to examine the interconnections of the cross-modal information of each
  organ region. Extensive experiments an1d comparisons with state-of-the-art
  methods across various evaluation metrics demonstrate the superior performance
  of our proposed method.
paper_ai_summary: The ORID framework improves radiology report generation by integrating organ-specific information using LLaVA-Med and Graph Neural Networks, reducing noise from unrelated organs and enhancing performance.
paper_ai_keywords: LaLaVA-Med encoder-decoder model Organ-Regional Information Driven (ORID) framework organ-based cross-modal fusion module organ importance coefficient analysis module Graph Neural Network (GNN)