ArticlePattern recognition2021
Multi-task contrastive learning for automatic CT and X-ray diagnosis of COVID-19.
Article in Pattern recognition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
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- Discriminative feature learning for multiclass lung disease classification using contrastive learning.Frontiers in radiology · 2026Article
- Panoptic segmentation for complete labeling of fruit microstructure in 3D micro-CT images with deep learning.Plant phenomics (Washington, D.C.) · 2025Article
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- Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN.Scientific reports · 2023Article
- Momentum contrast transformer for COVID-19 diagnosis with knowledge distillation.Pattern recognition · 2023Article
- Deep Learning Methods for Interpretation of Pulmonary CT and X-ray Images in Patients with COVID-19-Related Lung Involvement: A Systematic Review.Journal of clinical medicine · 2023Review
- Broad learning solution for rapid diagnosis of COVID-19.Biomedical signal processing and control · 2023Article
- Self-supervised learning for medical image classification: a systematic review and implementation guidelines.NPJ digital medicine · 2023Review
- Histogram of Oriented Gradients meet deep learning: A novel multi-task deep network for 2D surgical image semantic segmentation.Medical image analysis · 2023Article
- Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans.Computers in biology and medicine · 2023Article
- Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network.Applied soft computing · 2023Article
- COVID-19-The Role of Artificial Intelligence, Machine Learning, and Deep Learning: A Newfangled.Archives of computational methods in engineering : state of the art reviews · 2023Review
- Preliminary Stages for COVID-19 Detection Using Image Processing.Diagnostics (Basel, Switzerland) · 2022Review
- Classification and visual explanation for COVID-19 pneumonia from CT images using triple learning.Scientific reports · 2022Article
- The CP-ABM approach for modelling COVID-19 infection dynamics and quantifying the effects of non-pharmaceutical interventions.Pattern recognition · 2022Article
- Comprehensive Survey of Machine Learning Systems for COVID-19 Detection.Journal of imaging · 2022Review
- AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breath.Pattern recognition · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computed tomography (CT) and X-ray are effective methods for diagnosing COVID-19. Although several studies have demonstrated the potential of deep learning in the automatic diagnosis of COVID-19 using CT and X-ray, the generalization on unseen samples needs to be improved. To tackle this problem, we present the contrastive multi-task convolutional neural network (CMT-CNN), which is composed of two tasks. The main task is to diagnose COVID-19 from other pneumonia and normal control. The auxiliary task is to encourage local aggregation though a contrastive loss: first, each image is transformed by a series of augmentations (Poisson noise, rotation, etc.). Then, the model is optimized to embed representations of a same image similar while different images dissimilar in a latent space. In this way, CMT-CNN is capable of making transformation-invariant predictions and the spread-out properties of data are preserved. We demonstrate that the apparently simple auxiliary task provides powerful supervisions to enhance generalization. We conduct experiments on a CT dataset (4,758 samples) and an X-ray dataset (5,821 samples) assembled by open datasets and data collected in our hospital. Experimental results demonstrate that contrastive learning (as plugin module) brings solid accuracy improvement for deep learning models on both CT (5.49%-6.45%) and X-ray (0.96%-2.42%) without requiring additional annotations. Our codes are accessible online.
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