Cited by 493Multimodal Machine Learning ApplicationsImage Retrieval and Classification TechniquesDomain Adaptation and Few-Shot Learning

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GLoRIA: A Multimodal Global-Local Representation Learning Framework for Label-efficient Medical Image Recognition

Huang, Shih-Cheng, Shen, Liyue, Lungren, Matthew P., Yeung, Serena·2021·2021 IEEE/CVF International Conference on Computer Vision (ICCV)

DOI 10.1109/iccv48922.2021.00391

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Abstract

In recent years, the growing utilization of medical imaging is placing an increasing burden on radiologists. Deep learning provides a promising solution for automatic medical image analysis and clinical decision support. However, large-scale manually labeled datasets required for training deep neural networks are difficult and expensive to obtain for medical images. The purpose of this work is to develop label-efficient multimodal medical imaging representations by leveraging radiology reports. We propose an attention-based framework for learning global and local representations by contrasting image sub-regions and words in the paired report. In addition, we propose methods to leverage the learned representations for various downstream medical image recognition tasks with limited labels. Our results demonstrate high-performance and label-efficiency for image-text retrieval, classification (finetuning and zerosshot settings), and segmentation on different datasets.

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Authors: Huang, S.-C., Shen, L., Lungren, M. P., & Yeung, S.
Affiliations: Stanford University (US)
ORCID: Huang, Shih-Cheng
Shen, Liyue
Lungren, Matthew P.
Yeung, Serena
Venue: 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Publisher: IEEE
Published: 2021-10
Pages: 3922-3931
DOI: 10.1109/iccv48922.2021.00391
OpenAlex: W3201906559
Cited by: 489
References: 41
Citations by year: 2026: 122 · 2025: 195 · 2024: 106 · 2023: 51 · 2022: 14 · 2021: 1
FWCI: 14.04
Type: conference paper
Language: en
Open access: closed
Licence: doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords: multimodal representation learning, label-efficient learning, medical image recognition, radiology reports, image-text retrieval, zero-shot classification, medical image segmentation, limited labeled data

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GLoRIA: A Multimodal Global-Local Representation Learning Framework for Label-efficient Medical Image Recognition

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