Evidence map›Paper›PMID 36848308›Full record

ArticleMedical image understanding and analysis : 26th annual conference, MIUA 2022, Cambridge, UK, July 27-29, 2022, proceedings. Medical Image Understanding and Analysis (Conference) (26th : 2022 : Cambridge, England)2022

Weakly Supervised Captioning of Ultrasound Images.

Mohammad Alsharid, Harshita Sharma, Lior Drukker, Aris T Papageorgiou, J Alison Noble

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Article in Medical image understanding and analysis : 26th annual conference, MIUA 2022, Cambridge, UK, July 27-29, 2022, proceedings. Medical Image Understanding and Analysis (Conference) (26th : 2022 : Cambridge, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Mohammad AlsharidInstitute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
Harshita SharmaInstitute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
Lior DrukkerNuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK.
Aris T PapageorgiouNuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK.
J Alison NobleInstitute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medical image captioning models generate text to describe the semantic contents of an image, aiding the non-experts in understanding and interpretation. We propose a weakly-supervised approach to improve the performance of image captioning models on small image-text datasets by leveraging a large anatomically-labelled image classification dataset. Our method generates pseudo-captions (weak labels) for caption-less but anatomically-labelled (class-labelled) images using an encoder-decoder sequence-to-sequence model. The augmented dataset is used to train an image-captioning model in a weakly supervised learning manner. For fetal ultrasound, we demonstrate that the proposed augmentation approach outperforms the baseline on semantics and syntax-based metrics, with nearly twice as much improvement in value on

Indexed as

Data AugmentationFetal UltrasoundImage Captioning

Identifiers

PMID36848308
PMCPMC7614238

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.