Evidence map›Paper›PMID 33426152›Full record

ArticleJournal of medical imaging (Bellingham, Wash.)2021

Instance segmentation for whole slide imaging: end-to-end or detect-then-segment.

Aadarsh Jha, Haichun Yang, Ruining Deng, Meghan E Kapp, Agnes B Fogo, Yuankai Huo

Abstract read
In one paragraph

Article in Journal of medical imaging (Bellingham, Wash.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. AI applications in renal pathology.Kidney international · 2021
    Review
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.

Aadarsh JhaVanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, United States.ORCID https://orcid.org/0000-0001-9848-3925
Haichun YangVanderbilt University Medical Center, Department of Pathology, Microbiology and Immunology, Nashville, United States.ORCID https://orcid.org/0000-0003-4265-7492
Ruining DengVanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, United States.ORCID https://orcid.org/0000-0001-6300-8518
Meghan E KappVanderbilt University Medical Center, Department of Pathology, Microbiology and Immunology, Nashville, United States.ORCID https://orcid.org/0000-0002-0252-3919
Agnes B FogoVanderbilt University Medical Center, Department of Pathology, Microbiology and Immunology, Nashville, United States.
Yuankai HuoVanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, United States.ORCID https://orcid.org/0000-0002-2096-8065

Funding

RESOLUTION OF GLOMERULOSCLEROSISR01DK056942 · NIDDK · VANDERBILT UNIVERSITY MEDICAL CENTER · PI FOGO, AGNES B. · 2001 to 2021
$4.9M
NIDDK NIH HHS R01 DK056942
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

deep learningGlomeruliMask-RCNNsegmentationU-Netwhole slide imaging

Identifiers

PMID33426152
PMCPMC7790159

What OpenQuestion holds

Textmetadata
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.