Multi-modality Regional Alignment Network for Covid X-Ray Survival Prediction and Report Generation

• URL: http://arxiv.org/abs/2405.14113v1
• Date: Thu, 23 May 2024 02:41:08 GMT
• Title: Multi-modality Regional Alignment Network for Covid X-Ray Survival Prediction and Report Generation
• Authors: Zhusi Zhong, Jie Li, John Sollee, Scott Collins, Harrison Bai, Paul Zhang, Terrence Healey, Michael Atalay, Xinbo Gao, Zhicheng Jiao,
• Abstract summary: This study proposes Multi-modality Regional Alignment Network (MRANet), an explainable model for radiology report generation and survival prediction. MRANet visually grounds region-specific descriptions, providing robust anatomical regions with a completion strategy. A cross LLMs alignment is employed to enhance the image-to-text transfer process, resulting in sentences rich with clinical detail and improved explainability for radiologist.
• Score: 36.343753593390254
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In response to the worldwide COVID-19 pandemic, advanced automated technologies have emerged as valuable tools to aid healthcare professionals in managing an increased workload by improving radiology report generation and prognostic analysis. This study proposes Multi-modality Regional Alignment Network (MRANet), an explainable model for radiology report generation and survival prediction that focuses on high-risk regions. By learning spatial correlation in the detector, MRANet visually grounds region-specific descriptions, providing robust anatomical regions with a completion strategy. The visual features of each region are embedded using a novel survival attention mechanism, offering spatially and risk-aware features for sentence encoding while maintaining global coherence across tasks. A cross LLMs alignment is employed to enhance the image-to-text transfer process, resulting in sentences rich with clinical detail and improved explainability for radiologist. Multi-center experiments validate both MRANet's overall performance and each module's composition within the model, encouraging further advancements in radiology report generation research emphasizing clinical interpretation and trustworthiness in AI models applied to medical studies. The code is available at https://github.com/zzs95/MRANet.

Related papers

• D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions [8.50767187405446]
  We propose D-Rax -- a domain-specific, conversational, radiologic assistance tool. We enhance the conversational analysis of chest X-ray (CXR) images to support radiological reporting. We observe statistically significant improvement in responses when evaluated for both open and close-ended conversations.
  arXiv  Detail & Related papers  (2024-07-02T18:43:10Z)
• Large Model driven Radiology Report Generation with Clinical Quality Reinforcement Learning [16.849933628738277]
  Radiology report generation (RRG) has attracted significant attention due to its potential to reduce the workload of radiologists. This paper introduces a novel RRG method, textbfLM-RRG, that integrates large models (LMs) with clinical quality reinforcement learning. Experiments on the MIMIC-CXR and IU-Xray datasets demonstrate the superiority of our method over the state of the art.
  arXiv  Detail & Related papers  (2024-03-11T13:47:11Z)
• HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context Interaction [16.060286162384536]
  HistGen is a learning-empowered framework for histopathology report generation. It aims to boost report generation by aligning whole slide images (WSIs) and diagnostic reports from local and global granularity. Experimental results on WSI report generation show the proposed model outperforms state-of-the-art (SOTA) models by a large margin.
  arXiv  Detail & Related papers  (2024-03-08T15:51:43Z)
• 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)
• 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)
• XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models [60.437091462613544]
  We introduce XrayGPT, a novel conversational medical vision-language model. It can analyze and answer open-ended questions about chest radiographs. We generate 217k interactive and high-quality summaries from free-text radiology reports.
  arXiv  Detail & Related papers  (2023-06-13T17:59:59Z)
• 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)
• Cross-modal Memory Networks for Radiology Report Generation [30.13916304931662]
  Cross-modal memory networks (CMN) are proposed to enhance the encoder-decoder framework for radiology report generation. Our model is able to better align information from radiology images and texts so as to help generating more accurate reports in terms of clinical indicators.
  arXiv  Detail & Related papers  (2022-04-28T02:32:53Z)
• 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)
• 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.