Anatomy-Guided Radiology Report Generation with Pathology-Aware Regional Prompts

• URL: http://arxiv.org/abs/2411.10789v1
• Date: Sat, 16 Nov 2024 12:36:20 GMT
• Title: Anatomy-Guided Radiology Report Generation with Pathology-Aware Regional Prompts
• Authors: Yijian Gao, Dominic Marshall, Xiaodan Xing, Junzhi Ning, Giorgos Papanastasiou, Guang Yang, Matthieu Komorowski,
• Abstract summary: Radiology reporting generative AI holds significant potential to alleviate clinical workloads and streamline medical care. Existing systems often fall short due to their reliance on fixed size, patch-level image features and insufficient incorporation of pathological information. We propose an innovative approach that leverages pathology-aware regional prompts to explicitly integrate anatomical and pathological information of various scales.
• Score: 3.1019279528120363
• License:
• Abstract: Radiology reporting generative AI holds significant potential to alleviate clinical workloads and streamline medical care. However, achieving high clinical accuracy is challenging, as radiological images often feature subtle lesions and intricate structures. Existing systems often fall short, largely due to their reliance on fixed size, patch-level image features and insufficient incorporation of pathological information. This can result in the neglect of such subtle patterns and inconsistent descriptions of crucial pathologies. To address these challenges, we propose an innovative approach that leverages pathology-aware regional prompts to explicitly integrate anatomical and pathological information of various scales, significantly enhancing the precision and clinical relevance of generated reports. We develop an anatomical region detector that extracts features from distinct anatomical areas, coupled with a novel multi-label lesion detector that identifies global pathologies. Our approach emulates the diagnostic process of radiologists, producing clinically accurate reports with comprehensive diagnostic capabilities. Experimental results show that our model outperforms previous state-of-the-art methods on most natural language generation and clinical efficacy metrics, with formal expert evaluations affirming its potential to enhance radiology practice.

Related papers

• TopOC: Topological Deep Learning for Ovarian and Breast Cancer Diagnosis [3.262230127283452]
  Topological data analysis offers a unique approach by extracting essential information through the evaluation of topological patterns across different color channels. We show that the inclusion of topological features significantly improves the differentiation of tumor types in ovarian and breast cancers.
  arXiv  Detail & Related papers  (2024-10-13T12:24:13Z)
• ONCOPILOT: A Promptable CT Foundation Model For Solid Tumor Evaluation [3.8763197858217935]
  ONCOPILOT is an interactive radiological foundation model trained on approximately 7,500 CT scans covering the whole body. It performs 3D tumor segmentation using visual prompts like point-click and bounding boxes, outperforming state-of-the-art models. ONCOPILOT also accelerates measurement processes and reduces inter-reader variability.
  arXiv  Detail & Related papers  (2024-10-10T13:36:49Z)
• A review of handcrafted and deep radiomics in neurological diseases: transitioning from oncology to clinical neuroimaging [2.651601515140236]
  Radiomics is a methodology aimed at extracting quantitative information from imaging data. This paper presents a review of the radiomic pipeline from the clinical perspective. It discusses the application of handcrafted and deep radiomics in neuroimaging, stratified by neurological diagnosis.
  arXiv  Detail & Related papers  (2024-07-18T16:12:07Z)
• Potential of Multimodal Large Language Models for Data Mining of Medical Images and Free-text Reports [51.45762396192655]
  Multimodal large language models (MLLMs) have recently transformed many domains, significantly affecting the medical field. Notably, Gemini-Vision-series (Gemini) and GPT-4-series (GPT-4) models have epitomized a paradigm shift in Artificial General Intelligence for computer vision. This study evaluated the performance of the Gemini, GPT-4, and 4 popular large models for an exhaustive evaluation across 14 medical imaging datasets.
  arXiv  Detail & Related papers  (2024-07-08T09:08:42Z)
• ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data [115.0747462486285]
  ChatRadio-Valuer is a tailored model for automatic radiology report generation that learns generalizable representations. The clinical dataset utilized in this study encompasses a remarkable total of textbf332,673 observations. ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al.
  arXiv  Detail & Related papers  (2023-10-08T17:23:17Z)
• Act Like a Radiologist: Radiology Report Generation across Anatomical Regions [50.13206214694885]
  X-RGen is a radiologist-minded report generation framework across six anatomical regions. In X-RGen, we seek to mimic the behaviour of human radiologists, breaking them down into four principal phases. We enhance the recognition capacity of the image encoder by analysing images and reports across various regions.
  arXiv  Detail & Related papers  (2023-05-26T07:12:35Z)
• Self adaptive global-local feature enhancement for radiology report generation [10.958641951927817]
  We propose a novel framework AGFNet to dynamically fuse the global and anatomy region feature to generate multi-grained radiology report. Firstly, we extract important anatomy region features and global features of input Chest X-ray (CXR) Then, with the region features and the global features as input, our proposed self-adaptive fusion gate module could dynamically fuse multi-granularity information. Finally, the captioning generator generates the radiology reports through multi-granularity features.
  arXiv  Detail & Related papers  (2022-11-21T11:50:42Z)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)

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.