ArticleJournal of medical systems2019
Urine Sediment Recognition Method Based on Multi-View Deep Residual Learning in Microscopic Image.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers, 1 of them a synthesis that pooled it.
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
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Diagnostic Accuracy of Wireless Capsule Endoscopy in Polyp Recognition Using Deep Learning: A Meta-Analysis.International journal of clinical practice · 2022Pooled it
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- Enhancing physician support in pancreatic cancer diagnosis: New M-F-RCNN artificial intelligence model using endoscopic ultrasound.Endoscopy international open · 2024Article
- Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning.European radiology · 2023Article
- Automated Urine Cell Image Classification Model Using Chaotic Mixer Deep Feature Extraction.Journal of digital imaging · 2023Article
- Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model.Diagnostics (Basel, Switzerland) · 2023Article
- Automatic tumor segmentation and metachronous single-organ metastasis prediction of nasopharyngeal carcinoma patients based on multi-sequence magnetic resonance imaging.Frontiers in oncology · 2023Article
- Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study.European radiology · 2022Article
- Deep Learning Radiomics Nomogram to Predict Lung Metastasis in Soft-Tissue Sarcoma: A Multi-Center Study.Frontiers in oncology · 2022Article
- AI based colorectal disease detection using real-time screening colonoscopy.Precision clinical medicine · 2021Article
- CNN-GRU-AM for Shared Bicycles Demand Forecasting.Computational intelligence and neuroscience · 2021Article
- Medical Image Recognition Technology in the Effect of Substituting Soybean Meal for Fish Meal on the Diversity of Intestinal Microflora inJournal of healthcare engineering · 2021Article
- Calcium Spark Detection and Event-Based Classification of Single Cardiomyocyte Using Deep Learning.Frontiers in physiology · 2021Article
- Visual Interpretability in Computer-Assisted Diagnosis of Thyroid Nodules Using Ultrasound Images.Medical science monitor : international medical journal of experimental and clinical research · 2020Article
- A Microfluidic Detection System for Bladder Cancer Tumor Cells.Micromachines · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Urine sediment recognition is attracting growing interest in the field of computer vision. A multi-view urine cell recognition method based on multi-view deep residual learning is proposed to solve some existing problems, such as multi-view cell gray change and cell information loss in the natural state. Firstly, the convolutional network is designed to extract the urine sediment features from different perspectives based on the residual network, and the depth-wise separable convolution is introduced to reduce the network parameters. Secondly, Squeeze-and-Excitation block is embedded to learn feature weights, using feature re-calibration to improve network representation, and the robustness of the network is enhanced by adding spatial pyramid pooling. Finally, for further optimizing the recognition results, the Adam with weight decay optimization method is used to accelerate the convergence of the network model. Experiments on self-built urine microscopic image data-set show that our proposed method has state-of-the-art classification accuracy and reduces network computing time.
Indexed as
Identifiers
What OpenQuestion holds
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.