Evidence map›Paper›PMID 36282980›Full record

ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

Multimodal Representation Learning via Maximization of Local Mutual Information.

Ruizhi Liao, Daniel Moyer, Miriam Cha, Keegan Quigley, Seth Berkowitz, Steven Horng, Polina Golland, William M Wells

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Review
  2. Sample-Specific Debiasing for Better Image-Text Models.Proceedings of machine learning research · 2023
    Article
  3. Using Multiple Instance Learning to Build Multimodal Representations.Information processing in medical imaging : proceedings of the ... conference · 2023
    Article
  4. That's the Wrong Lung! Evaluating and Improving the Interpretability of Unsupervised Multimodal Encoders for Medical Data.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2022
    Article
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

8 authors.

Ruizhi LiaoCSAIL, Massachusetts Institute of Technology, Cambridge, MA, USA.
Daniel MoyerCSAIL, Massachusetts Institute of Technology, Cambridge, MA, USA.
Miriam ChaMIT Lincoln Laboratory, Lexington, MA, USA.
Keegan QuigleyMIT Lincoln Laboratory, Lexington, MA, USA.
Seth BerkowitzBeth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Steven HorngBeth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Polina GollandCSAIL, Massachusetts Institute of Technology, Cambridge, MA, USA.
William M WellsCSAIL, Massachusetts Institute of Technology, Cambridge, MA, USA.

Funding

Training and DisseminationP41EB015902 · NIBIB · BRIGHAM AND WOMEN'S HOSPITAL · PI WELLS, WILLIAM M. · 2012 to 2022
$20.9M
TRD 3 - Enabling Technologies for Intraprocedural GuidanceP41EB028741 · NIBIB · BRIGHAM AND WOMEN'S HOSPITAL · PI Junichi Tokuda · 2021 to 2026
$10.7M
NIBIB NIH HHS P41 EB015902NIBIB NIH HHS P41 EB028741
6 · The paper itself

Abstract

We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text. The goal of this approach is to learn

Indexed as

Local feature representationsMultimodal representation learningMutual information maximization

Identifiers

PMID36282980
PMCPMC9576150

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.