Source-linked AI summary
A Transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics
Hong-Yu Zhou, Yizhou Yu, Chengdi Wang, Shu Zhang, Yuanxu Gao, Jia Pan, Jun Shao, Guangming Lu, Kang Zhang, Weimin Li
TL;DR
Clinical diagnostic models have not adequately unified multimodal information such as complaints, images, and laboratory results. IRENE uses a unified Transformer to learn holistic representations from these inputs, supporting triage and clinical decision-making while requiring further validation of its clinical benefits.
Problem
Existing multimodal diagnostic workflows rely on cumbersome, labor- and time-intensive structuralization of unstructured clinical text.
Method
IRENE uses a unified Transformer with multimodal attention to progressively learn holistic representations from clinical images and structured and unstructured data.
Results
IRENE learns holistic multimodal representations while eliminating separate modality-specific processing paths used by non-unified methods.
Takeaways & Limitations
IRENE may help streamline patient triage and serve as a diagnostic aid in cases of clinical uncertainty or complexity.
Takeaways & Limitations
Current datasets are limited in size and diversity, and IRENE’s clinical benefits require further multi-institutional and multinational validation.
Abstract
from arXiv · showhide
During the diagnostic process, clinicians leverage multimodal information, such as chief complaints, medical images, and laboratory-test results. Deep-learning models for aiding diagnosis have yet to meet this requirement. Here we report a Transformer-based representation-learning model as a clinical diagnostic aid that processes multimodal input in a unified manner. Rather than learning modality-specific features, the model uses embedding layers to convert images and unstructured and structured text into visual tokens and text tokens, and bidirectional blocks with intramodal and intermodal attention to learn a holistic representation of radiographs, the unstructured chief complaint and clinical history, structured clinical information such as laboratory-test results and patient demographic information. The unified model outperformed an image-only model and non-unified multimodal diagnosis models in the identification of pulmonary diseases (by 12% and 9%, respectively) and in the prediction of adverse clinical outcomes in patients with COVID-19 (by 29% and 7%, respectively). Leveraging unified multimodal Transformer-based models may help streamline triage of patients and facilitate the clinical decision process.
IRENE provides a better Transformer-based choice for jointly interpreting multimodal clinical
IRENE jointly interprets multimodal clinical information through fused representations rather than simple concatenation or modality-specific processing. It also reduces reliance on manually engineered text structuralization pipelines used in traditional non-unified approaches.
- Multimodal representation learning: IRENE is compared with GIT and Perceiver, representative Transformer-based models that fuse multimodal information for classification.GIT uses common semantic information among modalities as supervision during multimodal pre-training.
- Multimodal representation learning: IRENE learns fused representations among modality-specific low-level embeddings, whereas Perceiver concatenates multimodal inputs into a single 1D sequence.Concatenation can give greater influence to modalities with more tokens, such as images relative to text.
- Text processing: IRENE reduces reliance on text structuralization in traditional non-unified multimodal medical diagnosis workflows.These workflows typically convert unstructured text into structured fields before diagnosis.
- Text processing: Recent text structuralization pipelines rely heavily on artificial rules and modern NLP tools to extract structured fields from unstructured texts.Human annotators manually define alternate spellings, synonyms, and abbreviations for structured labels before specialized NLP extraction.
Outlook
The study presents IRENE as a unified multimodal diagnostic Transformer that learns holistic representations from clinical data. It may support patient triage and clinical decision-making, but deployment requires addressing limitations in dataset size and diversity.
- Contribution: IRENE combines a unified multimodal diagnostic Transformer with bidirectional multimodal attention blocks to learn holistic representations.This distinguishes it from previous non-unified methods described in the conclusion.
- Clinical application: IRENE may help streamline patient care by triaging patients and distinguishing common-cold cases from those needing urgent intervention.The passage frames this as a potential real-world application.
- Clinical application: Further refinement could enable these frameworks to assist physicians with diagnostic uncertainty or complexity.The passage suggests such systems might support and enhance physician reasoning.
- Limitations: IRENE deployment must address datasets limited in size and diversity to improve generalization across broader clinical settings.The proposed remedy is collecting data from additional institutions, medical devices, countries, and ethnic groups.