ORID: Organ-Regional Information Driven Framework for Radiology Report
  Generation
Authors: name: Tiancheng Gu, name: Kaicheng Yang, name: Xiang An, name: Ziyong Feng, name: Dongnan Liu, name: Weidong Cai

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.