Evidence map›Paper›PMID 35885617›Full record

ArticleDiagnostics (Basel, Switzerland)2022

Improving the Diagnosis of Skin Biopsies Using Tissue Segmentation.

Shima Nofallah, Beibin Li, Mojgan Mokhtari, Wenjun Wu, Stevan Knezevich, Caitlin J May, Oliver H Chang, Joann G Elmore, Linda G Shapiro

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2022. 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
0.7field-weighted citation impact, top 28% of its field
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, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Automated analysis of whole slide digital skin biopsy images.Frontiers in artificial intelligence · 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

9 authors at 5 institutions in 2 countries.

Shima NofallahDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195, USA.
Beibin LiPaul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA.ORCID 0000-0002-4426-3449
Mojgan MokhtariPathology Department, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran.
Wenjun WuDepartment of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA.
Stevan KnezevichPathology Associates, Clovis, CA 983611, USA.
Caitlin J MayDermatopathology Northwest, Bellevue, WA 98005, USA.
Oliver H ChangDepartment of Pathology, University of Washington, Seattle, WA 98195, USA.
Joann G ElmoreDavid Geffen School of Medicine, UCLA, Los Angeles, CA 90024, USA.
Linda G ShapiroDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195, USA.ORCID 0000-0002-9495-0968
University of Washington · USCentral Dermatology · USIsfahan University of Medical Sciences · IRUniversity of California, Los Angeles · USUniversity of Washington Medical Center · US

Funding

Accuracy in the Diagnosis of Melanoma & the Impact of Double ReadingR01CA151306 · NCI · UNIVERSITY OF WASHINGTON · PI ELMORE, JOANN G · 2011 to 2015
$4.1M
Improving Melanoma Pathology Accuracy through Computer Vision Techniques - the IMPACT StudyR01CA200690 · NCI · UNIVERSITY OF WASHINGTON · PI ELMORE, JOANN G, SHAPIRO, LINDA · 2016 to 2020
$1.9M
A Unified Machine Learning Package for Cancer DiagnosisU01CA231782 · NCI · UNIVERSITY OF WASHINGTON · PI ELMORE, JOANN G, SHAPIRO, LINDA · 2018 to 2020
$980k
Melanoma Research Alliance 622600NCI NIH HHS R01CA151306NCI NIH HHS R01CA200690NCI NIH HHS U01CA231782United States Department of Defense W81XWH-20-1-0798
6 · The paper itself

Abstract

Invasive melanoma, a common type of skin cancer, is considered one of the deadliest. Pathologists routinely evaluate melanocytic lesions to determine the amount of atypia, and if the lesion represents an invasive melanoma, its stage. However, due to the complicated nature of these assessments, inter- and intra-observer variability among pathologists in their interpretation are very common. Machine-learning techniques have shown impressive and robust performance on various tasks including healthcare. In this work, we study the potential of including semantic segmentation of clinically important tissue structure in improving the diagnosis of skin biopsy images. Our experimental results show a 6% improvement in F-score when using whole slide images along with epidermal nests and cancerous dermal nest segmentation masks compared to using whole-slide images alone in training and testing the diagnosis pipeline.

Indexed as

accuracymachine learningmelanoma diagnosissemantic segmentationskin biopsytransformerswhole slide imaging

Identifiers

PMID35885617
PMCPMC9316584
OpenAlexW4285597143

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.