ArticleJournal of healthcare engineering2022
Automatic Segmentation of Magnetic Resonance Images of Severe Patients with Advanced Liver Cancer and the Molecular Mechanism of Emodin-Induced Apoptosis of HepG2 Cells under the Deep Learning.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artifact estimation network for MR images: effectiveness of batch normalization and dropout layers.BMC medical imaging · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To improve the accuracy of clinical diagnosis of severe patients with advanced liver cancer and enhance the effect of chemotherapy treatment, the U-Net model was optimized by introducing the batch normalization (BN) layer and the dropout layer, and the segmentation training and verification of the optimized model were realized by the magnetic resonance (MR) image data. Subsequently, HepG2 cells were taken as the research objects and treated with 0, 10, 20, 40, 60, 80, and 100
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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.