MDF-Net for abnormality detection by fusing X-rays with clinical data

• URL: http://arxiv.org/abs/2302.13390v3
• Date: Wed, 27 Dec 2023 18:04:27 GMT
• Title: MDF-Net for abnormality detection by fusing X-rays with clinical data
• Authors: Chihcheng Hsieh and Isabel Blanco Nobre and Sandra Costa Sousa and Chun Ouyang and Margot Brereton and Jacinto C. Nascimento and Joaquim Jorge and Catarina Moreira
• Abstract summary: This study investigates the effects of including patients' clinical information on the performance of deep learning (DL) classifiers for disease location in chest X-rays. We propose a novel architecture consisting of two fusion methods that enable the model to simultaneously process patients' clinical data and chest X-rays. Results show that incorporating patients' clinical data in a DL model together with the proposed fusion methods improves the disease localization in chest X-rays by 12% in terms of Average Precision.
• Score: 14.347359031598813
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This study investigates the effects of including patients' clinical information on the performance of deep learning (DL) classifiers for disease location in chest X-ray images. Although current classifiers achieve high performance using chest X-ray images alone, our interviews with radiologists indicate that clinical data is highly informative and essential for interpreting images and making proper diagnoses. In this work, we propose a novel architecture consisting of two fusion methods that enable the model to simultaneously process patients' clinical data (structured data) and chest X-rays (image data). Since these data modalities are in different dimensional spaces, we propose a spatial arrangement strategy, spatialization, to facilitate the multimodal learning process in a Mask R-CNN model. We performed an extensive experimental evaluation using MIMIC-Eye, a dataset comprising modalities: MIMIC-CXR (chest X-ray images), MIMIC IV-ED (patients' clinical data), and REFLACX (annotations of disease locations in chest X-rays). Results show that incorporating patients' clinical data in a DL model together with the proposed fusion methods improves the disease localization in chest X-rays by 12\% in terms of Average Precision compared to a standard Mask R-CNN using only chest X-rays. Further ablation studies also emphasize the importance of multimodal DL architectures and the incorporation of patients' clinical data in disease localization. The architecture proposed in this work is publicly available to promote the scientific reproducibility of our study (https://github.com/ChihchengHsieh/multimodal-abnormalities-detection)

Related papers

• MedPromptX: Grounded Multimodal Prompting for Chest X-ray Diagnosis [1.2903829793534272]
  Chest X-ray images are commonly used for predicting acute and chronic cardiopulmonary conditions. Efforts to integrate them with structured clinical data face challenges due to incomplete electronic health records. This paper introduces MedPromptX, the first model to integrate multimodal large language models (MLLMs), few-shot prompting (FP) and visual grounding (VG) Results demonstrate the SOTA performance of MedPromptX, achieving an 11% improvement in F1-score compared to the baselines.
  arXiv  Detail & Related papers  (2024-03-22T19:19:51Z)
• MLVICX: Multi-Level Variance-Covariance Exploration for Chest X-ray Self-Supervised Representation Learning [6.4136876268620115]
  MLVICX is an approach to capture rich representations in the form of embeddings from chest X-ray images. We demonstrate the performance of MLVICX in advancing self-supervised chest X-ray representation learning.
  arXiv  Detail & Related papers  (2024-03-18T06:19:37Z)
• 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)
• UMedNeRF: Uncertainty-aware Single View Volumetric Rendering for Medical Neural Radiance Fields [38.62191342903111]
  We propose an Uncertainty-aware MedNeRF (UMedNeRF) network based on generated radiation fields. We show the results of CT projection rendering with a single X-ray and compare our method with other methods based on generated radiation fields.
  arXiv  Detail & Related papers  (2023-11-10T02:47:15Z)
• 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)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• MMLN: Leveraging Domain Knowledge for Multimodal Diagnosis [10.133715767542386]
  We propose a knowledge-driven and data-driven framework for lung disease diagnosis. We formulate diagnosis rules according to authoritative clinical medicine guidelines and learn the weights of rules from text data. A multimodal fusion consisting of text and image data is designed to infer the marginal probability of lung disease.
  arXiv  Detail & Related papers  (2022-02-09T04:12:30Z)
• The pitfalls of using open data to develop deep learning solutions for COVID-19 detection in chest X-rays [64.02097860085202]
  Deep learning models have been developed to identify COVID-19 from chest X-rays. Results have been exceptional when training and testing on open-source data. Data analysis and model evaluations show that the popular open-source dataset COVIDx is not representative of the real clinical problem.
  arXiv  Detail & Related papers  (2021-09-14T10:59:11Z)
• 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)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)

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.