ArticleComputer methods and programs in biomedicine2023
MMViT-Seg: A lightweight transformer and CNN fusion network for COVID-19 segmentation.
Article in Computer methods and programs in biomedicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Manual segmentation of opacities and consolidations on CT of long COVID patients from multiple annotators.Scientific data · 2025Article
- MT-SCnet: multi-scale token divided and spatial-channel fusion transformer network for microscopic hyperspectral image segmentation.Frontiers in oncology · 2024Article
- Hybrid transformer-CNN and LSTM model for lung disease segmentation and classification.PeerJ. Computer science · 2024Article
- Fully feature fusion based neural network for COVID-19 lesion segmentation in CT images.Biomedical signal processing and control · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveCOVID-19 is a serious threat to human health. Traditional convolutional neural networks (CNNs) can realize medical image segmentation, whilst transformers can be used to perform machine vision tasks, because they have a better ability to capture long-range relationships than CNNs. The combination of CNN and transformers to complete the task of semantic segmentation has attracted intense research. Currently, it is challenging to segment medical images on limited data sets like that on COVID-19.
methodsThis study proposes a lightweight transformer+CNN model, in which the encoder sub-network is a two-path design that enables both the global dependence of image features and the low layer spatial details to be effectively captured. Using CNN and MobileViT to jointly extract image features reduces the amount of computation and complexity of the model as well as improves the segmentation performance. So this model is titled Mini-MobileViT-Seg (MMViT-Seg). In addition, a multi query attention (MQA) module is proposed to fuse the multi-scale features from different levels of decoder sub-network, further improving the performance of the model. MQA can simultaneously fuse multi-input, multi-scale low-level feature maps and high-level feature maps as well as conduct end-to-end supervised learning guided by ground truth.
resultsThe two-class infection labeling experiments were conducted based on three datasets. The final results show that the proposed model has the best performance and the minimum number of parameters among five popular semantic segmentation algorithms. In multi-class infection labeling results, the proposed model also achieved competitive performance.
conclusionsThe proposed MMViT-Seg is tested on three COVID-19 segmentation datasets, with results showing that this model has better performance than other models. In addition, the proposed MQA module, which can effectively fuse multi-scale features of different levels further improves the segmentation accuracy.
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.