Evidence map›Paper›PMID 30243216›Full record

ArticleComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society2018

An EM-based semi-supervised deep learning approach for semantic segmentation of histopathological images from radical prostatectomies.

Jiayun Li, William Speier, King Chung Ho, Karthik V Sarma, Arkadiusz Gertych, Beatrice S Knudsen, Corey W Arnold

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Article
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  10. Review
  11. Classifying Breast Histopathology Images with a Ductal Instance-Oriented Pipeline.Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition · 2021
    Article
  12. Review
  13. Article
  14. Artificial intelligence at the intersection of pathology and radiology in prostate cancer.Diagnostic and interventional radiology (Ankara, Turkey) · 2019
    Review
  15. Article
  16. Article
  17. Article
  18. 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

7 authors.

Jiayun LiDepartment of Bioengineering, University of California, Los Angeles, CA, USA; Computational Integrated Diagnostics, Departments of Radiological Sciences and Pathology and Laboratory Medicine, University of California, Los Angeles, CA, USA.
William SpeierComputational Integrated Diagnostics, Departments of Radiological Sciences and Pathology and Laboratory Medicine, University of California, Los Angeles, CA, USA.
King Chung HoDepartment of Bioengineering, University of California, Los Angeles, CA, USA; Computational Integrated Diagnostics, Departments of Radiological Sciences and Pathology and Laboratory Medicine, University of California, Los Angeles, CA, USA.
Karthik V SarmaDepartment of Bioengineering, University of California, Los Angeles, CA, USA; Computational Integrated Diagnostics, Departments of Radiological Sciences and Pathology and Laboratory Medicine, University of California, Los Angeles, CA, USA.
Arkadiusz GertychDepartment of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Beatrice S KnudsenDepartment of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Corey W ArnoldDepartment of Bioengineering, University of California, Los Angeles, CA, USA; Computational Integrated Diagnostics, Departments of Radiological Sciences and Pathology and Laboratory Medicine, University of California, Los Angeles, CA, USA. Electronic address: cwarnold@ucla.edu.

Funding

UCLA SPORE IN PROSTATE CANCERP50CA092131 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI REITER, ROBERT E · 2002 to 2023
$42.6M
UCLA-Caltech Medical Scientist Training ProgramT32GM008042 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI AJIJOLA, OLUJIMI A, DAWSON, DAVID WAYNE · 1985 to 2023
$29.9M
Medical Imaging Informatics Training GrantT32EB016640 · NIBIB · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ALEX BUI, William Hsu · 2013 to 2026
$2.3M
Predicting the Presence of Clinically Significant Prostate Cancer using Multiparametric MRI and MR-US Fusion BiopsyR21CA220352 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARNOLD, COREY WELLS · 2018 to 2019
$352k
Development of a Multimodal Deep Learning Model for the Generation of Cancer Probability Maps and Imaging Biomarkers for Prostate Cancer using Multiparametric MRIF30CA210329 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SARMA, KARTHIK VENKATARAMAN · 2016 to 2022
$173k
NCI NIH HHS F30 CA210329NCI NIH HHS P50 CA092131NCI NIH HHS R21 CA220352NIBIB NIH HHS T32 EB016640
6 · The paper itself

Abstract

Automated Gleason grading is an important preliminary step for quantitative histopathological feature extraction. Different from the traditional task of classifying small pre-selected homogeneous regions, semantic segmentation provides pixel-wise Gleason predictions across an entire slide. Deep learning-based segmentation models can automatically learn visual semantics from data, which alleviates the need for feature engineering. However, performance of deep learning models is limited by the scarcity of large-scale fully annotated datasets, which can be both expensive and time-consuming to create. One way to address this problem is to leverage external weakly labeled datasets to augment models trained on the limited data. In this paper, we developed an expectation maximization-based approach constrained by an approximated prior distribution in order to extract useful representations from a large number of weakly labeled images generated from low-magnification annotations. This method was utilized to improve the performance of a model trained on a limited fully annotated dataset. Our semi-supervised approach trained with 135 fully annotated and 1800 weakly annotated tiles achieved a mean Jaccard Index of 49.5% on an independent test set, which was 14% higher than the initial model trained only on the fully annotated dataset.

Indexed as

Deep LearningProstatectomySupervised Machine LearningAlgorithmsHumansImage Processing, Computer-AssistedMaleProstateProstatic NeoplasmsExpectation maximizationHistopathological image segmentationProstate cancerSemi-supervised deep learning

Identifiers

PMID30243216
PMCPMC6173982

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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.