COVID-19 detection from scarce chest X-Ray image data using few-shot deep learning approach
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In the current COVID-19 pandemic situation, there is an urgent need to screen infected patients quickly and accurately. Using deep learning models trained on chest X-ray images can become an efficient method for screening COVID-19 patients in these situations. Deep learning approaches are already widely used in the medical community. However, they require a large amount of data to be accurate.
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COVID-19 detection from scarce chest X-Ray image data using few-shot deep learning approach
- 1. COVID-19 detection from scarce chest X-Ray image data using few- shot deep learning approach By Shruti Jadon Machine Learning Researcher M.S. UMass Amherst, SPIE Member
- 2. Covid-19: Statistics ● As of Feb 2021, ~2.2 million died due to covid. Alone in US, 450k lost their battle to covid. ● There is a shortage of front line workers. ● Due to impact on economy, many people lost their livelihood.
- 3. How Deep Learning can help? Deep Learning can: 1. Fasten the process of detection. 2. Reduce the dependency on front-line doctors and nurses. 3. Prediction in early stages can save lives and health.
- 4. Conditions of using Deep Learning Models ● Require Large amount of Data ● Computation Power ● Training Time
- 5. How to deal with data scarcity problem? To train a good model with less data, we need: 1. Better Embeddings or 1. Better Optimizer
- 6. Modeling approaches for Covid classification We have performed experiments using following models: 1. Logistic Regression (Baseline) 2. Convolutional Neural Networks. 3. Transfer Learning 4. Unsupervised Learning (clustering) 5. Few-Shot Learning (Siamese Networks)
- 7. CNN and Transfer Learning
- 8. Unsupervised Learning approach 1. Doesn’t require labeling. 1. Uses Distance as measure in embedding space to determine the class of a new data input. 1. Suffers from curse of dimensionality. 1. Dimensionality Reduction approaches help in resolving and creating better clusters
- 9. Custom Siamese Network (Our proposed model) A Siamese network, as the name suggests, is an architecture with two parallel layers. It compares two inputs based on a similarity metric and checks whether they are the same or not. This network consists of two identical neural networks, which share similar parameters, each head taking one input data point. The last layers of these networks are fed to a contrastive loss function layer, which calculates the similarity between the two inputs.
- 13. Conclusion 1. Change is data distribution may result in low accuracy: When data is scarce, it's tough to determine the real distribution of data, even if we segregate data into train, test, and val. We might not be able to capture the performance of model on real data distribution. 1. General Classification models might not work: In case of less data, generally unsupervised based approaches perform well. In our experiments, we have observed that the decision boundaries were more segregated in the PCA+TSNE and Siamese Network approach, whereas for Logistic Regression, CNN, and Transfer Learning score observed is below 0.2 for both K Means and GMM clustering approaches.
- 14. Thank you !