A Clinically-Grounded Two-Stage Framework for Renal CT Report Generation

• URL: http://arxiv.org/abs/2506.23584v1
• Date: Mon, 30 Jun 2025 07:45:02 GMT
• Title: A Clinically-Grounded Two-Stage Framework for Renal CT Report Generation
• Authors: Renjie Liang, Zhengkang Fan, Jinqian Pan, Chenkun Sun, Russell Terry, Jie Xu,
• Abstract summary: We propose a two-stage framework for generating renal radiology reports from 2D CT slices.<n>First, we extract structured abnormality features using a multi-task learning model trained to identify lesion attributes.<n>These extracted features are combined with the corresponding CT image and fed into a fine-tuned vision-language model to generate natural language report sentences.
• Score: 2.988064755409503
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating radiology reports from CT scans remains a complex task due to the nuanced nature of medical imaging and the variability in clinical documentation. In this study, we propose a two-stage framework for generating renal radiology reports from 2D CT slices. First, we extract structured abnormality features using a multi-task learning model trained to identify lesion attributes such as location, size, enhancement, and attenuation. These extracted features are subsequently combined with the corresponding CT image and fed into a fine-tuned vision-language model to generate natural language report sentences aligned with clinical findings. We conduct experiments on a curated dataset of renal CT studies with manually annotated sentence-slice-feature triplets and evaluate performance using both classification metrics and natural language generation metrics. Our results demonstrate that the proposed model outperforms random baselines across all abnormality types, and the generated reports capture key clinical content with reasonable textual accuracy. This exploratory work highlights the feasibility of modular, feature-informed report generation for renal imaging. Future efforts will focus on extending this pipeline to 3D CT volumes and further improving clinical fidelity in multimodal medical AI systems.

Related papers

• OrthoInsight: Rib Fracture Diagnosis and Report Generation Based on Multi-Modal Large Models [0.49478969093606673]
  We propose OrthoInsight, a multi-modal deep learning framework for rib fracture diagnosis and report generation.<n>It integrates a YOLOv9 model for fracture detection, a medical knowledge graph for retrieving clinical context, and a fine-tuned LLaVA language model for generating diagnostic reports.<n> evaluated on 28,675 annotated CT images and expert reports, it achieves high performance across Diagnostic Accuracy, Content Completeness, Logical Coherence, and Clinical Guidance Value, with an average score of 4.28.
  arXiv  Detail & Related papers  (2025-07-18T15:01:44Z)
• Interactive Segmentation and Report Generation for CT Images [10.23242820828816]
  We propose a novel interactive framework for 3D lesion morphology reporting.<n>We are the first to integrate the interactive segmentation and structured reports in 3D CT medical images.
  arXiv  Detail & Related papers  (2025-03-05T09:18:27Z)
• 3D-CT-GPT: Generating 3D Radiology Reports through Integration of Large Vision-Language Models [51.855377054763345]
  This paper introduces 3D-CT-GPT, a Visual Question Answering (VQA)-based medical visual language model for generating radiology reports from 3D CT scans. Experiments on both public and private datasets demonstrate that 3D-CT-GPT significantly outperforms existing methods in terms of report accuracy and quality.
  arXiv  Detail & Related papers  (2024-09-28T12:31:07Z)
• Structural Entities Extraction and Patient Indications Incorporation for Chest X-ray Report Generation [10.46031380503486]
  We introduce a novel method, textbfStructural textbfEntities extraction and patient indications textbfIncorporation (SEI) for chest X-ray report generation. We employ a structural entities extraction (SEE) approach to eliminate presentation-style vocabulary in reports. We propose a cross-modal fusion network to integrate information from X-ray images, similar historical cases, and patient-specific indications.
  arXiv  Detail & Related papers  (2024-05-23T01:29:47Z)
• Dia-LLaMA: Towards Large Language Model-driven CT Report Generation [4.634780391920529]
  We propose Dia-LLaMA, a framework to adapt the LLaMA2-7B for CT report generation by incorporating diagnostic information as guidance prompts. Considering the high dimension of CT, we leverage a pre-trained ViT3D with perceiver to extract the visual information. To tailor the LLM for report generation and emphasize abnormality, we extract additional diagnostic information by referring to a disease prototype memory bank.
  arXiv  Detail & Related papers  (2024-03-25T03:02:51Z)
• Radiology Report Generation Using Transformers Conditioned with Non-imaging Data [55.17268696112258]
  This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information. The proposed network uses a convolutional neural network to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information.
  arXiv  Detail & Related papers  (2023-11-18T14:52:26Z)
• Beyond Images: An Integrative Multi-modal Approach to Chest X-Ray Report Generation [47.250147322130545]
  Image-to-text radiology report generation aims to automatically produce radiology reports that describe the findings in medical images. Most existing methods focus solely on the image data, disregarding the other patient information accessible to radiologists. We present a novel multi-modal deep neural network framework for generating chest X-rays reports by integrating structured patient data, such as vital signs and symptoms, alongside unstructured clinical notes.
  arXiv  Detail & Related papers  (2023-11-18T14:37:53Z)
• Medical Image Captioning via Generative Pretrained Transformers [57.308920993032274]
  We combine two language models, the Show-Attend-Tell and the GPT-3, to generate comprehensive and descriptive radiology records. The proposed model is tested on two medical datasets, the Open-I, MIMIC-CXR, and the general-purpose MS-COCO.
  arXiv  Detail & Related papers  (2022-09-28T10:27:10Z)
• Cross-modal Clinical Graph Transformer for Ophthalmic Report Generation [116.87918100031153]
  We propose a Cross-modal clinical Graph Transformer (CGT) for ophthalmic report generation (ORG) CGT injects clinical relation triples into the visual features as prior knowledge to drive the decoding procedure. Experiments on the large-scale FFA-IR benchmark demonstrate that the proposed CGT is able to outperform previous benchmark methods.
  arXiv  Detail & Related papers  (2022-06-04T13:16:30Z)
• Factored Attention and Embedding for Unstructured-view Topic-related Ultrasound Report Generation [70.7778938191405]
  We propose a novel factored attention and embedding model (termed FAE-Gen) for the unstructured-view topic-related ultrasound report generation. The proposed FAE-Gen mainly consists of two modules, i.e., view-guided factored attention and topic-oriented factored embedding, which capture the homogeneous and heterogeneous morphological characteristic across different views.
  arXiv  Detail & Related papers  (2022-03-12T15:24:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.