Evidence map›Paper›PMID 37174600›Full record

ArticleAnimals : an open access journal from MDPI2023

Canine Mammary Tumor Histopathological Image Classification via Computer-Aided Pathology: An Available Dataset for Imaging Analysis.

Giovanni P Burrai, Andrea Gabrieli, Marta Polinas, Claudio Murgia, Maria Paola Becchere, Pierfranco Demontis, Elisabetta Antuofermo

Abstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. 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

7 authors.

Giovanni P BurraiDepartment of Veterinary Medicine, University of Sassari, Via Vienna 2, 07100 Sassari, Italy.ORCID 0000-0001-7696-7478
Andrea GabrieliDepartment of Veterinary Medicine, University of Sassari, Via Vienna 2, 07100 Sassari, Italy.ORCID 0000-0003-1583-2566
Marta PolinasDepartment of Veterinary Medicine, University of Sassari, Via Vienna 2, 07100 Sassari, Italy.
Claudio MurgiaDepartment of Veterinary Medicine, University of Sassari, Via Vienna 2, 07100 Sassari, Italy.
Maria Paola BecchereIndependent Researcher, 07100 Sassari, Italy.
Pierfranco DemontisDepartment of Chemical, Physical, Mathematical and Natural Sciences, University of Sassari, Via Vienna 2, 07100 Sassari, Italy.
Elisabetta AntuofermoDepartment of Veterinary Medicine, University of Sassari, Via Vienna 2, 07100 Sassari, Italy.

Funding

Università degli Studi di Sassari, fondo di Ateneo per la ricerca 2019-2020 fondo di Ateneo per la ricerca 2019-2020
6 · The paper itself

Abstract

Histopathology, the gold-standard technique in classifying canine mammary tumors (CMTs), is a time-consuming process, affected by high inter-observer variability. Digital (DP) and Computer-aided pathology (CAD) are emergent fields that will improve overall classification accuracy. In this study, the ability of the CAD systems to distinguish benign from malignant CMTs has been explored on a dataset-namely

Indexed as

breast cancercanine mammary tumor (CMTs)CMT datasetdeep learninghistological classificationmachine learning

Identifiers

PMID37174600
PMCPMC10177203

What OpenQuestion holds

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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.