CT-AGRG: Automated Abnormality-Guided Report Generation from 3D Chest CT Volumes

• URL: http://arxiv.org/abs/2408.11965v4
• Date: Wed, 30 Oct 2024 13:22:45 GMT
• Title: CT-AGRG: Automated Abnormality-Guided Report Generation from 3D Chest CT Volumes
• Authors: Theo Di Piazza,
• Abstract summary: Existing methods typically generate entire reports directly from 3D CT images, without explicitly focusing on observed abnormalities. We propose a new anomaly-guided report generation model, which first predicts abnormalities and then generates targeted descriptions for each.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid increase of computed tomography (CT) scans and their time-consuming manual analysis have created an urgent need for robust automated analysis techniques in clinical settings. These aim to assist radiologists and help them managing their growing workload. Existing methods typically generate entire reports directly from 3D CT images, without explicitly focusing on observed abnormalities. This unguided approach often results in repetitive content or incomplete reports, failing to prioritize anomaly-specific descriptions. We propose a new anomaly-guided report generation model, which first predicts abnormalities and then generates targeted descriptions for each. Evaluation on a public dataset demonstrates significant improvements in report quality and clinical relevance. We extend our work by conducting an ablation study to demonstrate its effectiveness.

Related papers

• CT-GRAPH: Hierarchical Graph Attention Network for Anatomy-Guided CT Report Generation [4.376648893167674]
  We propose CT-GRAPH, a hierarchical graph attention network that explicitly models radiological knowledge.<n>Our method leverages pretrained 3D medical feature encoders to obtain global and organ-level features.<n>We show that our method achieves a substantial improvement of absolute 7.9% in F1 score over current state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-08-07T13:18:03Z)
• A Clinically-Grounded Two-Stage Framework for Renal CT Report Generation [2.988064755409503]
  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.
  arXiv  Detail & Related papers  (2025-06-30T07:45:02Z)
• Imitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classification [0.0]
  Multi-label classification of 3D CT scans is a challenging task due to the volumetric nature of the data and the variety of anomalies to be detected. Existing deep learning methods based on Convolutional Neural Networks (CNNs) struggle to capture long-range dependencies effectively. We present CT-Scroll, a novel global-local attention model specifically designed to emulate the scrolling behavior of radiologists during the analysis of 3D CT scans.
  arXiv  Detail & Related papers  (2025-03-26T15:47:50Z)
• 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)
• HERGen: Elevating Radiology Report Generation with Longitudinal Data [18.370515015160912]
  We propose a novel History Enhanced Radiology Report Generation (HERGen) framework to efficiently integrate longitudinal data across patient visits. Our approach not only allows for comprehensive analysis of varied historical data but also improves the quality of generated reports through an auxiliary contrastive objective. The extensive evaluations across three datasets demonstrate that our framework surpasses existing methods in generating accurate radiology reports and effectively predicting disease progression from medical images.
  arXiv  Detail & Related papers  (2024-07-21T13:29:16Z)
• 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)
• Attributed Abnormality Graph Embedding for Clinically Accurate X-Ray Report Generation [7.118069629513661]
  We introduce a novel fined-grained knowledge graph structure called an attributed abnormality graph (ATAG) The ATAG consists of interconnected abnormality nodes and attribute nodes, allowing it to better capture the abnormality details. We show that the proposed ATAG-based deep model outperforms the SOTA methods by a large margin and can improve the clinical accuracy of the generated reports.
  arXiv  Detail & Related papers  (2022-07-04T05:32:00Z)
• 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)
• Explainable multiple abnormality classification of chest CT volumes with AxialNet and HiResCAM [89.2175350956813]
  We introduce the challenging new task of explainable multiple abnormality classification in volumetric medical images. We propose a multiple instance learning convolutional neural network, AxialNet, that allows identification of top slices for each abnormality. We then aim to improve the model's learning through a novel mask loss that leverages HiResCAM and 3D allowed regions.
  arXiv  Detail & Related papers  (2021-11-24T01:14:33Z)
• Contrastive Attention for Automatic Chest X-ray Report Generation [124.60087367316531]
  In most cases, the normal regions dominate the entire chest X-ray image, and the corresponding descriptions of these normal regions dominate the final report. We propose Contrastive Attention (CA) model, which compares the current input image with normal images to distill the contrastive information. We achieve the state-of-the-art results on the two public datasets.
  arXiv  Detail & Related papers  (2021-06-13T11:20:31Z)
• Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation [55.00308939833555]
  The PPKED includes three modules: Posterior Knowledge Explorer (PoKE), Prior Knowledge Explorer (PrKE) and Multi-domain Knowledge Distiller (MKD) PoKE explores the posterior knowledge, which provides explicit abnormal visual regions to alleviate visual data bias. PrKE explores the prior knowledge from the prior medical knowledge graph (medical knowledge) and prior radiology reports (working experience) to alleviate textual data bias.
  arXiv  Detail & Related papers  (2021-06-13T11:10:02Z)
• Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest X-rays [6.686095511538683]
  This work focuses on reporting abnormal findings on radiology images. We propose a method to identify abnormal findings from the reports in addition to grouping them with unsupervised clustering and minimal rules. We demonstrate that our method is able to retrieve abnormal findings and outperforms existing generation models on both clinical correctness and text generation metrics.
  arXiv  Detail & Related papers  (2020-10-06T04:18:18Z)
• Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report Generation [107.3538598876467]
  We propose an Auxiliary Signal-Guided Knowledge-Decoder (ASGK) to mimic radiologists' working patterns. ASGK integrates internal visual feature fusion and external medical linguistic information to guide medical knowledge transfer and learning.
  arXiv  Detail & Related papers  (2020-06-06T01:00:15Z)

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.