PadChest: A large chest x-ray image dataset with multi-label annotated reports [PDF]
, 2019 We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports.
Bustos, Aurelia +3 more
core +2 more sources
A Proactive Agent Collaborative Framework for Zero‐Shot Multimodal Medical Reasoning
Advanced Intelligent Systems, EarlyView.This work proposes a multimodal medical collaborative reasoning framework, which imitates clinician's working patterns of comparative analysis. The framework includes a cohort of domain‐expert models, and an large language model learner agent to generate inquiries and interact with these experts to gather the essential information, and integrate the ...
Zishan Gu +4 more
wiley +1 more source
Pleural clinic: where thoracic ultrasound meets respiratory medicine
Frontiers in Medicine, 2023 Thoracic ultrasound (TUS) has become an essential procedure in respiratory medicine. Due to its intrinsic safety and versatility, it has been applied in patients affected by several respiratory diseases both in intensive care and outpatient settings. TUS
Mariaenrica Tinè +8 more
doaj +1 more source
Weakly-Supervised Segmentation for Disease Localization in Chest X-Ray Images [PDF]
arXiv, 2020 Deep Convolutional Neural Networks have proven effective in solving the task of semantic segmentation. However, their efficiency heavily relies on the pixel-level annotations that are expensive to get and often require domain expertise, especially in medical imaging.
arxiv
Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks [PDF]
arXiv, 2021 Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in coronavirus disease (COVID-19) patients, but are not part of the clinical routine since required manual segmentation of lung lesions is prohibitively time-consuming.
arxiv
Survival prediction in mesothelioma using a scalable lasso regression model: instructions for use and initial performance using clinical predictors [PDF]
, 2018 Introduction: Accurate prognostication is difficult in malignant pleural mesothelioma (MPM). We developed a set of robust computational models to quantify the prognostic value of routinely available clinical data, which form the basis of published MPM ...
Blyth, Kevin G. +5 more
core +1 more source
Advanced Intelligent Systems, EarlyView.The radiomics feature could save the storage space of all medical samples; on the other hand, it avoids data leakage. Graph convolutional neural networks could summarize the similarity of benign and malignant pulmonary nodules to improve the performance in distinguishing them with radiomics and common clinical features.
Renjie Xu +7 more
wiley +1 more source
An approach to study recruitment/derecruitment dynamics in a patient-specific computational model of an injured human lung [PDF]
arXiv, 2022 We present a new approach for physics-based computational modeling of diseased human lungs. Our main object is the development of a model that takes the novel step of incorporating the dynamics of airway recruitment/de-recruitment into an anatomically accurate, spatially resolved model of respiratory system mechanics, and the relation of these dynamics
arxiv
Design, Modeling, and Control of a Soft Robotic Diaphragm‐Assist Device in a Respiratory Simulator
Advanced Intelligent Systems, EarlyView.A soft robotic diaphragm‐assist device using fabric‐based pneumatic actuators with a 2‐step control system to optimize synchronization and support is introduced. This system can detect the initiation of breathing to trigger assistance and regulate pressures to provide the correct level of inhalation augmentation. Validation and testing are completed on
Diego Quevedo‐Moreno +5 more
wiley +1 more source
Development of pericardial fat count images using a combination of three different deep-learning models [PDF]
arXiv, 2023 Rationale and Objectives: Pericardial fat (PF), the thoracic visceral fat surrounding the heart, promotes the development of coronary artery disease by inducing inflammation of the coronary arteries. For evaluating PF, this study aimed to generate pericardial fat count images (PFCIs) from chest radiographs (CXRs) using a dedicated deep-learning model.
arxiv