A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics
During the diagnostic process, clinicians leverage multimodal information, such as the chief
complaint, medical images and laboratory test results. Deep-learning models for aiding …
complaint, medical images and laboratory test results. Deep-learning models for aiding …
Multimodal deep learning for integrating chest radiographs and clinical parameters: a case for transformers
Background Clinicians consider both imaging and nonimaging data when diagnosing
diseases; however, current machine learning approaches primarily consider data from a …
diseases; however, current machine learning approaches primarily consider data from a …
A medical multimodal large language model for future pandemics
Deep neural networks have been integrated into the whole clinical decision procedure
which can improve the efficiency of diagnosis and alleviate the heavy workload of …
which can improve the efficiency of diagnosis and alleviate the heavy workload of …
Multi-task vision transformer using low-level chest X-ray feature corpus for COVID-19 diagnosis and severity quantification
Developing a robust algorithm to diagnose and quantify the severity of the novel coronavirus
disease 2019 (COVID-19) using Chest X-ray (CXR) requires a large number of well-curated …
disease 2019 (COVID-19) using Chest X-ray (CXR) requires a large number of well-curated …
An interpretable deep learning workflow for discovering subvisual abnormalities in CT scans of COVID-19 inpatients and survivors
L Zhou, X Meng, Y Huang, K Kang, J Zhou… - Nature Machine …, 2022 - nature.com
Tremendous efforts have been made to improve diagnosis and treatment of COVID-19, but
knowledge on long-term complications is limited. In particular, a large portion of survivors …
knowledge on long-term complications is limited. In particular, a large portion of survivors …
COVID-19 automatic diagnosis with radiographic imaging: Explainable attention transfer deep neural networks
Researchers seek help from deep learning methods to alleviate the enormous burden of
reading radiological images by clinicians during the COVID-19 pandemic. However …
reading radiological images by clinicians during the COVID-19 pandemic. However …
A comparison of pre-trained vision-and-language models for multimodal representation learning across medical images and reports
Joint image-text embedding extracted from medical images and associated contextual
reports is the bedrock for most biomedical vision-and-language (V+ L) tasks, including …
reports is the bedrock for most biomedical vision-and-language (V+ L) tasks, including …
Multi-task driven explainable diagnosis of COVID-19 using chest X-ray images
With increasing number of COVID-19 cases globally, all the countries are ramping up the
testing numbers. While the RT-PCR kits are available in sufficient quantity in several …
testing numbers. While the RT-PCR kits are available in sufficient quantity in several …
BiomedGPT: a unified and generalist biomedical generative pre-trained transformer for vision, language, and multimodal tasks
In this paper, we introduce a unified and generalist Biomedical Generative Pre-trained
Transformer (BiomedGPT) model, which leverages self-supervision on large and diverse …
Transformer (BiomedGPT) model, which leverages self-supervision on large and diverse …
Covid-19 prognosis via self-supervised representation learning and multi-image prediction
The rapid spread of COVID-19 cases in recent months has strained hospital resources,
making rapid and accurate triage of patients presenting to emergency departments a …
making rapid and accurate triage of patients presenting to emergency departments a …