ArticleIEEE transactions on industrial informatics2021
An Effective Deep Neural Network for Lung Lesions Segmentation From COVID-19 CT Images.
Article in IEEE transactions on industrial informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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.
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
21 citing papers in PubMed.
- OpenVocabCT: Toward Universal Text-Driven CT Image Segmentation.IEEE transactions on medical imaging · 2026Article
- Automated segmentation of COVID-19 lesions in CT scans using attention U-net with hybrid loss functions.Scientific reports · 2026Article
- Sparse scanning encoding and neural network decoding for compressed photoacoustic microscopy.Photoacoustics · 2025Article
- Lung Segmentation with Lightweight Convolutional Attention Residual U-Net.Diagnostics (Basel, Switzerland) · 2025Article
- Segmenting and classifying lung diseases with M-Segnet and Hybrid Squeezenet-CNN architecture on CT images.PloS one · 2024Article
- Deep Learning Framework for Liver Segmentation fromSensors (Basel, Switzerland) · 2023Article
- SuperMini-seg: An ultra lightweight network for COVID-19 lung infection segmentation from CT images.Biomedical signal processing and control · 2023Article
- Modification of a Conventional Deep Learning Model to Classify Simulated Breathing Patterns: A Step toward Real-Time Monitoring of Patients with Respiratory Infectious Diseases.Sensors (Basel, Switzerland) · 2023Article
- MIC-Net: A deep network for cross-site segmentation of COVID-19 infection in the fog-assisted IoMT.Information sciences · 2023Article
- Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.Sensors (Basel, Switzerland) · 2023Review
- Exploiting multi-granularity visual features for retinal layer segmentation in human eyes.Frontiers in bioengineering and biotechnology · 2023Article
- Deep Learning Techniques to Diagnose Lung Cancer.Cancers · 2022Review
- A dual-stage deep convolutional neural network for automatic diagnosis of COVID-19 and pneumonia from chest CT images.Computers in biology and medicine · 2022Article
- Automatic lung segmentation in chest X-ray images using improved U-Net.Scientific reports · 2022Article
- Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning.Sensors (Basel, Switzerland) · 2022Article
- Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks.Computers in biology and medicine · 2022Review
- COVID-view: Diagnosis of COVID-19 using Chest CT.IEEE transactions on visualization and computer graphics · 2022Article
- Deep learning based model for classification of COVID -19 images for healthcare research progress.Materials today. Proceedings · 2022Article
- DW-UNet: Loss Balance under Local-Patch for 3D Infection Segmentation from COVID-19 CT Images.Diagnostics (Basel, Switzerland) · 2021Article
- Automatic Detection of Colorectal Polyps Using Transfer Learning.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automatic segmentation of lung lesions from COVID-19 computed tomography (CT) images can help to establish a quantitative model for diagnosis and treatment. For this reason, this article provides a new segmentation method to meet the needs of CT images processing under COVID-19 epidemic. The main steps are as follows: First, the proposed region of interest extraction implements patch mechanism strategy to satisfy the applicability of 3-D network and remove irrelevant background. Second, 3-D network is established to extract spatial features, where 3-D attention model promotes network to enhance target area. Then, to improve the convergence of network, a combination loss function is introduced to lead gradient optimization and training direction. Finally, data augmentation and conditional random field are applied to realize data resampling and binary segmentation. This method was assessed with some comparative experiment. By comparison, the proposed method reached the highest performance. Therefore, it has potential clinical applications.
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.