Cited by 8Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and EducationImage Retrieval and Classification Techniques

The DOI matches its Crossref record; 31 of the 32 references it cites were found in a registry or an index.

Pre-trained multilevel fuse network based on vision-conditioned reasoning and bilinear attentions for medical image visual question answering

Cai, Linqin, Fang, Haodu, Li, Zhiqing·2023·The Journal of Supercomputing

Overview

Integrity signals

Registry

Crossref · 10.1007/s11227-023-05195-2

Cites

31 of 31 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 32 references

Research works printed but not linked: 15

  1. Chebbi I (2021) Chabbiimen at vqa-med 2021: visual generation of relevant natural language questions from radiology images for anomaly detection. In: CLEF (Working Notes), pp. 1201–1210
  2. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Advances in neural information processing systems, pp 91–99
  3. Wu J, Mooney R (2019) Self-critical reasoning for robust visual question answering. Advances in Neural Information Processing Systems, pp 8604–8614
  4. Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J, et al. (2021) Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, PMLR pp. 8748–8763
  5. Cadene R, Dancette C, Cord M, Parikh D, et al (2019) Rubi: Reducing unimodal biases for visual question answering. Advances in neural information processing systems, pp 841–852
  6. Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, PMLR, pp. 1126–1135
  7. Eslami S, de Melo G, Meinel C (2021) Does clip benefit visual question answering in the medical domain as much as it does in the general domain? arXiv preprint arXiv:2112.13906 · arXiv
  8. Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhudinov R, Zemel R, Bengio Y (2015) Show, attend and tell: neural image caption generation with visual attention. In: International Conference on Machine Learning, PMLR pp. 2048–2057
  9. Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I et al (2019) Language models are unsupervised multitask learners. OpenAI blog 1(8):9
  10. Riquelme C, Puigcerver J, Mustafa B, Neumann M, Jenatton R, Susano Pinto A, Keysers D, Houlsby N (2021) Scaling vision with sparse mixture of experts. Adv Neural Inf Process Sys 34:8583–8595
  11. Lu J, Yang J, Batra D, Parikh D (2016) Hierarchical question-image co-attention for visual question answering. Advances in neural information processing systems, pp 289–297
  12. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems, pp 6000–6010
  13. Kim J-H, Jun J, Zhang B-T (2018) Bilinear attention networks. Advances in neural information processing systems, pp 1571–1581
  14. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al (2020) An image is worth 16 x 16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 · arXiv
  15. Simpson AL, Antonelli M, Bakas S, Bilello M, Farahani K, Van Ginneken B, Kopp-Schneider A, Landman BA, Litjens G, Menze B, et al (2019) A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:1902.09063 · arXiv

Cited by

Sorted by citations

Metadata

Authors
Cai, L., Fang, H., & Li, Z.
Affiliations
Chongqing University of Posts and Telecommunications (CN)
Corresponding author
Fang, Haodu
Venue
The Journal of Supercomputing
Published
2023-03-29
Volume
79
Issue
12
Pages
13696-13723
ISSN
  • 0920-8542 (ISSN-L)
  • 1573-0484
OpenAlex
W4361284153
Cited by
8
References
32
Citations by year
2026: 1 · 2025: 4 · 2024: 3
FWCI
0.73
Type
journal article
Language
en
Open access
closed
Locations
The Journal of Supercomputing (publishedVersion)
Funders
  • National Natural Science Foundation of China (62277008)
  • Educational Informatization Project of Chongqing University of Posts and Telecommunications (xxhyf2022-08)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
Keywords
multimodal fusion, language bias, CLIP, BiLSTM

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review