ArticleJournal of pathology informatics2017
The Need for Careful Data Collection for Pattern Recognition in Digital Pathology.
Article in Journal of pathology informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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.
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Who cites it
8 citing papers in PubMed.
- [Chinese expert consensus on the technical and clinical practice specifications of artificial intelligence assisted morphology examination of blood cells (2024)].Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi · 2024Article
- Machine learning to detect the SINEs of cancer.Science translational medicine · 2024Article
- Performance comparison between multi-center histopathology datasets of a weakly-supervised deep learning model for pancreatic ductal adenocarcinoma detection.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Review
- Semantic annotation for computational pathology: multidisciplinary experience and best practice recommendations.The journal of pathology. Clinical research · 2022Article
- The need for measurement science in digital pathology.Journal of pathology informatics · 2022Article
- Open Practices and Resources for Collaborative Digital Pathology.Frontiers in medicine · 2019Review
- Gastric Pathology Image Classification Using Stepwise Fine-Tuning for Deep Neural Networks.Journal of healthcare engineering · 2018Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Effective pattern recognition requires carefully designed ground-truth datasets. In this technical note, we first summarize potential data collection issues in digital pathology and then propose guidelines to build more realistic ground-truth datasets and to control their quality. We hope our comments will foster the effective application of pattern recognition approaches in digital pathology.
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Registered trials
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.