Evidence map›Paper›PMID 38981594›Full record

ArticlePhysics in medicine and biology2024

Uncertainty quantification via localized gradients for deep learning-based medical image assessments.

Brayden Schott, Dmitry Pinchuk, Victor Santoro-Fernandes, Žan Klaneček, Luciano Rivetti, Alison Deatsch, Scott Perlman, Yixuan Li, Robert Jeraj

Abstract read
In one paragraph

Article in Physics in medicine and biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Brayden SchottDepartment of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States of America.ORCID 0009-0000-3326-1854
Dmitry PinchukDepartment of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States of America.
Victor Santoro-FernandesDepartment of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States of America.ORCID 0000-0001-6965-0448
Žan KlanečekFaculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia.ORCID 0000-0003-3409-7771
Luciano RivettiFaculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia.ORCID 0009-0001-0738-7485
Alison DeatschDepartment of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States of America.ORCID 0000-0002-4525-1327
Scott PerlmanDepartment of Radiology, Section of Nuclear Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States of America.ORCID 0000-0001-9744-7105
Yixuan LiDepartment of Computer Sciences, School of Computer, Data, & Information Sciences, University of Wisconsin, Madison, WI, United States of America.ORCID 0000-0003-3479-4323
Robert JerajDepartment of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States of America.ORCID 0000-0002-2192-2931

Funding

University of Wisconsin Prostate SPOREP50CA269011 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI DAVID F. JARRARD, DOUGLAS G. MCNEEL · 2023 to 2026
$10.6M
NCI NIH HHS P50 CA269011
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Deep LearningImage Processing, Computer-AssistedHumansUncertaintydeep learninggradient informationmetastatic diseasemodel reliabilitymodel trustworthinesstumor segmentationuncertainty quantification

Identifiers

PMID38981594
PMCPMC13037787

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.