AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |
Back to Blog
![]() ![]() In practice, according to whether multiple input modalities are fused at the feature or prediction level, these non-unified methods can be further categorized into early 19, 20, 21, 22 or late fusion 23 methods.Ī, Contrasting the previous non-unified multimodal diagnosis paradigm with IRENE. Finally, a fusion module is employed to unify these modality-specific features or predictions for making final diagnostic decisions. Then, data in different modalities are fed to different machine-learning models to produce modality-specific features or predictions. Next, a text structuralization process is introduced to transform the narrative text into structured tokens. Given a set of input data from different sources, these approaches first roughly divide them into three basic modalities, that is, images, narrative text (such as the chief complaint, which includes the history of present and past illness) and structured fields (for example, demographics and laboratory test results). 1a, current multimodal clinical decision support systems 19, 20, 21, 22, 23 mostly lean on a non-unified way to fuse information from multiple sources. Among these techniques, the development of deep learning 12, 13 endows machine-learning models with the ability to detect diseases from medical images near or at the level of human experts 14, 15, 16, 17, 18.Īlthough artificial intelligence (AI)-based medical image diagnosis has achieved tremendous progress in recent years, how to jointly interpret medical images and their associated clinical context remains a challenge. To meet the increasing demand for precision medicine, machine-learning techniques 11 have become the de facto choice for automatic yet intelligent medical diagnosis. Meanwhile, simply increasing the workload of experienced physicians and radiologists would inevitably exhaust their energy and thus increase the risk of misdiagnosis. The above multimodal diagnostic workflow requires substantial expertise, which may not be available in geographic regions with limited medical resources. ![]() The importance of exploiting multimodal clinical information has been extensively verified in the literature 3, 4, 5, 6, 7, 8, 9, 10 in different specialties, including but not limited to radiology, dermatology and ophthalmology. Then, physicians rely on their rich domain knowledge and years of training to make optimal diagnoses by jointly interpreting such multimodal data 1, 2. In practice, abnormal radiographic patterns are first associated with symptoms mentioned in the chief complaint or abnormal results in the laboratory test report. For instance, apart from chest radiographs, thoracic physicians need to take into account each patient’s demographics (such as age and gender), the chief complaint (such as history of present and past illness) and laboratory test reports to make accurate diagnostic decisions. ![]() It has been common practice in modern medicine to use multimodal clinical information for medical diagnosis. Unified multimodal transformer-based models may help streamline the triaging of patients and facilitate the clinical decision-making process. The unified model outperformed an image-only model and non-unified multimodal diagnosis models in the identification of pulmonary disease (by 12% and 9%, respectively) and in the prediction of adverse clinical outcomes in patients with COVID-19 (by 29% and 7%, respectively). Rather than learning modality-specific features, the model leverages embedding layers to convert images and unstructured and structured text into visual tokens and text tokens, and uses bidirectional blocks with intramodal and intermodal attention to learn holistic representations of radiographs, the unstructured chief complaint and clinical history, and structured clinical information such as laboratory test results and patient demographic information. ![]() Here we report a transformer-based representation-learning model as a clinical diagnostic aid that processes multimodal input in a unified manner. Deep-learning models for aiding diagnosis have yet to meet this requirement of leveraging multimodal information. During the diagnostic process, clinicians leverage multimodal information, such as the chief complaint, medical images and laboratory test results. ![]()
0 Comments
Read More
Leave a Reply. |