Attention based CNN-LSTM Network for Pulmonary Embolism Prediction on Chest Computed Tomography Pulmonary Angiograms

• URL: http://arxiv.org/abs/2107.06276v1
• Date: Tue, 13 Jul 2021 17:58:15 GMT
• Title: Attention based CNN-LSTM Network for Pulmonary Embolism Prediction on Chest Computed Tomography Pulmonary Angiograms
• Authors: Sudhir Suman, Gagandeep Singh, Nicole Sakla, Rishabh Gattu, Jeremy Green, Tej Phatak, Dimitris Samaras, Prateek Prasanna
• Abstract summary: Pulmonary Embolism (PE) is among the most fatal cardiovascular diseases. We propose a two-stage attention-based CNN-LSTM network for predicting PE. Our framework mirrors the radiologic diagnostic process via a multi-slice approach.
• Score: 22.62583095023903
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With more than 60,000 deaths annually in the United States, Pulmonary Embolism (PE) is among the most fatal cardiovascular diseases. It is caused by an artery blockage in the lung; confirming its presence is time-consuming and is prone to over-diagnosis. The utilization of automated PE detection systems is critical for diagnostic accuracy and efficiency. In this study we propose a two-stage attention-based CNN-LSTM network for predicting PE, its associated type (chronic, acute) and corresponding location (leftsided, rightsided or central) on computed tomography (CT) examinations. We trained our model on the largest available public Computed Tomography Pulmonary Angiogram PE dataset (RSNA-STR Pulmonary Embolism CT (RSPECT) Dataset, N=7279 CT studies) and tested it on an in-house curated dataset of N=106 studies. Our framework mirrors the radiologic diagnostic process via a multi-slice approach so that the accuracy and pathologic sequela of true pulmonary emboli may be meticulously assessed, enabling physicians to better appraise the morbidity of a PE when present. Our proposed method outperformed a baseline CNN classifier and a single-stage CNN-LSTM network, achieving an AUC of 0.95 on the test set for detecting the presence of PE in the study.

Related papers

• PECon: Contrastive Pretraining to Enhance Feature Alignment between CT and EHR Data for Improved Pulmonary Embolism Diagnosis [0.8213829427624407]
  We propose Pulmonary Embolism Detection using Contrastive Learning (PECon) PECon is a supervised contrastive pretraining strategy that employs both the patients CT scans as well as the EHR data. Results show that the proposed work outperforms the existing techniques and achieves state-of-the-art performance on the RadFusion dataset.
  arXiv  Detail & Related papers  (2023-08-27T09:07:26Z)
• Predicting Pulmonary Hypertension by Electrocardiograms Using Machine Learning [0.0]
  Pulmonary hypertension (PH) is a condition of high blood pressure that affects the arteries in the lungs and the right side of the heart. The goal of this project is to create a neural network model which can process an ECG signal and detect the presence of PH with a confidence probability.
  arXiv  Detail & Related papers  (2023-04-24T21:00:16Z)
• Anatomically aware dual-hop learning for pulmonary embolism detection in CT pulmonary angiograms [8.112976210963243]
  We introduce a deep learning based approach, which efficiently combines computer vision and deep neural networks for pulmonary embolism detection. Our method features novel improvements along three axes: 1) automatic detection of anatomical structures; 2) anatomical aware pretraining, neural and 3) a dual-hop deep net for PE detection.
  arXiv  Detail & Related papers  (2023-03-30T17:58:52Z)
• Convolutional Neural Network for Early Pulmonary Embolism Detection via Computed Tomography Pulmonary Angiography [0.0]
  The purpose of this study was to develop a computer-aided detection system for triaging patients with pulmonary embolism (PE) The proposed CAD system can distinguish between patients with and without PE and automatically label PE lesions.
  arXiv  Detail & Related papers  (2022-04-07T04:16:11Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning [133.87426554801252]
  We adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage.
  arXiv  Detail & Related papers  (2021-05-25T12:06:55Z)
• Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade oftwo U-nets: training and assessment on multipledatasets using different annotation criteria [83.83783947027392]
  This study aims at exploiting Artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. We developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets. The accuracy in predicting the CT-Severity Score (CT-SS) of the LungQuant system has been also evaluated.
  arXiv  Detail & Related papers  (2021-05-06T10:21:28Z)
• Fibrosis-Net: A Tailored Deep Convolutional Neural Network Design for Prediction of Pulmonary Fibrosis Progression from Chest CT Images [59.622239796473885]
  Pulmonary fibrosis is a chronic lung disease that causes irreparable lung tissue scarring and damage, resulting in progressive loss in lung capacity and no known cure. We introduce Fibrosis-Net, a deep convolutional neural network design tailored for the prediction of pulmonary fibrosis progression from chest CT images.
  arXiv  Detail & Related papers  (2021-03-06T02:16:41Z)
• M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging [85.00066186644466]
  We propose a Multi-task Multi-slice Deep Learning System (M3Lung-Sys) for multi-class lung pneumonia screening from CT imaging. In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M 3 Lung-Sys also be able to locate the areas of relevant lesions.
  arXiv  Detail & Related papers  (2020-10-07T06:22:24Z)
• Automatic Diagnosis of Pulmonary Embolism Using an Attention-guided Framework: A Large-scale Study [5.4009326643013065]
  Pulmonary Embolism (PE) is a life-threatening disorder associated with high mortality and morbidity. We explored a deep learning model to detect PE on volumetric contrast-enhanced chest CT scans using a 2-stage training strategy.
  arXiv  Detail & Related papers  (2020-05-29T20:46:24Z)
• Deep Learning for Automatic Pneumonia Detection [72.55423549641714]
  Pneumonia is the leading cause of death among young children and one of the top mortality causes worldwide. Computer-aided diagnosis systems showed the potential for improving diagnostic accuracy. We develop the computational approach for pneumonia regions detection based on single-shot detectors, squeeze-and-excitation deep convolution neural networks, augmentations and multi-task learning.
  arXiv  Detail & Related papers  (2020-05-28T10:54:34Z)

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.