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Contextualized Keyword Representations for Multi-modal Retinal Image Captioning

Authors:

Jia-Hong Huang,

Marcel WorringAuthors Info & Claims

ICMR '21: Proceedings of the 2021 International Conference on Multimedia Retrieval

Pages 645 - 652

https://doi.org/10.1145/3460426.3463667

Published: 01 September 2021 Publication History

Abstract

Medical image captioning automatically generates a medical description to describe the content of a given medical image. Traditional medical image captioning models create a medical description based on a single medical image input only. Hence, an abstract medical description or concept is hard to be generated based on the traditional approach. Such a method limits the effectiveness of medical image captioning. Multi-modal medical image captioning is one of the approaches utilized to address this problem. In multi-modal medical image captioning, textual input, e.g., expert-defined keywords, is considered as one of the main drivers of medical description generation. Thus, encoding the textual input and the medical image effectively are both important for the task of multi-modal medical image captioning. In this work, a new end-to-end deep multi-modal medical image captioning model is proposed. Contextualized keyword representations, textual feature reinforcement, and masked self-attention are used to develop the proposed approach. Based on the evaluation of an existing multi-modal medical image captioning dataset, experimental results show that the proposed model is effective with an increase of +53.2% in BLEU-avg and +18.6% in CIDEr, compared with the state-of-the-art method. https://github.com/Jhhuangkay/Contextualized-Keyword-Representations-for-Multi-modal-Retinal-Image-Captioning

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Cited By

Huang JGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Multi-modal Video SummarizationProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3657582(1214-1218)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3657582
Shaik NCherukuri TYe D(2024)M3T: Multi-Modal Medical Transformer To Bridge Clinical Context With Visual Insights For Retinal Image Medical Description Generation2024 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP51287.2024.10647584(3037-3043)Online publication date: 27-Oct-2024
https://doi.org/10.1109/ICIP51287.2024.10647584
Tang YYang HZhang LYuan Y(2024)Work like a doctorExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121442237:PAOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121442
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Index Terms

Contextualized Keyword Representations for Multi-modal Retinal Image Captioning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene understanding

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Information

Published In

cover image ACM Conferences

ICMR '21: Proceedings of the 2021 International Conference on Multimedia Retrieval

August 2021

715 pages

ISBN:9781450384636

DOI:10.1145/3460426

General Chairs:
Wen-Huang Cheng
National Yang Ming Chiao Tung University, Taiwan
,
Mohan Kankanhalli
National University of Singapore, Singapore
,
Meng Wang
Hefei University of Technology, China
,
Program Chairs:
Wei-Ta Chu
National Cheng Kung University, Taiwan
,
Jiaying Liu
Peking University, China
,
Marcel Worring
University of Amsterdam, Netherlands

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 01 September 2021

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ICMR '21: International Conference on Multimedia Retrieval

August 21 - 24, 2021

Taipei, Taiwan

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Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Huang JGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Multi-modal Video SummarizationProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3657582(1214-1218)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3657582
Shaik NCherukuri TYe D(2024)M3T: Multi-Modal Medical Transformer To Bridge Clinical Context With Visual Insights For Retinal Image Medical Description Generation2024 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP51287.2024.10647584(3037-3043)Online publication date: 27-Oct-2024
https://doi.org/10.1109/ICIP51287.2024.10647584
Tang YYang HZhang LYuan Y(2024)Work like a doctorExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121442237:PAOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121442
Tran TTran NDang TPham HNakashima Y(2024)ECG Captioning with Prior-Knowledge Transformer and Diffusion Probabilistic ModelJournal of Healthcare Informatics Research10.1007/s41666-024-00176-3Online publication date: 22-Oct-2024
https://doi.org/10.1007/s41666-024-00176-3
Bazi YRahhal MBashmal LZuair M(2023)Vision–Language Model for Visual Question Answering in Medical ImageryBioengineering10.3390/bioengineering1003038010:3(380)Online publication date: 20-Mar-2023
https://doi.org/10.3390/bioengineering10030380
Ghandi TPourreza HMahyar H(2023)Deep Learning Approaches on Image Captioning: A ReviewACM Computing Surveys10.1145/361759256:3(1-39)Online publication date: 5-Oct-2023
https://dl.acm.org/doi/10.1145/3617592
Wu THuang JLin JWorring M(2023)Expert-defined Keywords Improve Interpretability of Retinal Image Captioning2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV56688.2023.00190(1859-1868)Online publication date: Jan-2023
https://doi.org/10.1109/WACV56688.2023.00190
Kodavati TMohan GKumar RR E(2023)Image Captioning for Chest X-Rays Using GRU - Based Attention Mechanism2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)10.1109/ICCCIS60361.2023.10425174(518-525)Online publication date: 3-Nov-2023
https://doi.org/10.1109/ICCCIS60361.2023.10425174
Huang JYang CChen PChen MWorring M(2023)Causalainer: Causal Explainer for Automatic Video Summarization2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW59228.2023.00262(2630-2636)Online publication date: Jun-2023
https://doi.org/10.1109/CVPRW59228.2023.00262
Huang JWu TYang CShi ZLin ITegner JWorring M(2022)Non-local Attention Improves Description Generation for Retinal Images2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV51458.2022.00331(3250-3259)Online publication date: Jan-2022
https://doi.org/10.1109/WACV51458.2022.00331
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