Evidence map›Paper›PMID 38298808›Full record

Article... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision2023

Self-supervised Semantic Segmentation: Consistency over Transformation.

Sanaz Karimijafarbigloo, Reza Azad, Amirhossein Kazerouni, Yury Velichko, Ulas Bagci, Dorit Merhof

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

6 authors.

Sanaz KarimijafarbiglooFaculty of Informatics and Data Science, University of Regensburg, Germany.
Reza AzadFaculty of Electrical Engineering and Information Technology, RWTH Aachen University, Germany.
Amirhossein KazerouniSchool of Electrical Engineering, Iran University of Science and Technology, Iran.
Yury VelichkoDepartment of Radiology, Northwestern University, Chicago, USA.
Ulas BagciDepartment of Radiology, Northwestern University, Chicago, USA.
Dorit MerhofFaculty of Informatics and Data Science, University of Regensburg, Germany.

Funding

Data Coordinating Center for the Type 1 Diabetes in Acute Pancreatitis ConsortiumU01DK127384 · NIDDK · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI Vernon M Chinchilli, Temel Tirkes · 2020 to 2026
$21.3M
Cyst-X: Interpretable Deep Learning Based Risk Stratification of Pancreatic Cystic TumorsR01CA246704 · NCI · UNIVERSITY OF CENTRAL FLORIDA · PI BAGCI, ULAS · 2020 to 2024
$2.4M
Radiologist-Centered Artificial Intelligence (RCAI) for Lung Cancer Screening and DiagnosisR01CA240639 · NCI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI BAGCI, ULAS · 2020 to 2024
$2.0M
Hybrid Intelligence for Trustable Diagnosis And Patient Management of Prostate Cancer (HIT-PIRADS)U01CA268808 · NCI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI Ulas Bagci · 2023 to 2026
$1.5M
Application of machine learning for fast prediction of MRI-induced RF heating in patients with implanted conductive leadsR03EB032943 · NIBIB · NORTHWESTERN UNIVERSITY AT CHICAGO · PI BAGCI, ULAS, GOLESTANI RAD, LALEH · 2022 to 2023
$141k
NCI NIH HHS R01 CA240639NCI NIH HHS R01 CA246704NCI NIH HHS U01 CA268808NIBIB NIH HHS R03 EB032943NIDDK NIH HHS U01 DK127384
6 · The paper itself

Abstract

Accurate medical image segmentation is of utmost importance for enabling automated clinical decision procedures. However, prevailing supervised deep learning approaches for medical image segmentation encounter significant challenges due to their heavy dependence on extensive labeled training data. To tackle this issue, we propose a novel self-supervised algorithm,

Identifiers

PMID38298808
PMCPMC10829429

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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.