Summary of Multilabel Classification For Lung Disease Detection: Integrating Deep Learning and Natural Language Processing, by Maria Efimovich et al.
Multilabel Classification for Lung Disease Detection: Integrating Deep Learning and Natural Language Processing
by Maria Efimovich, Jayden Lim, Vedant Mehta, Ethan Poon
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed transfer learning model for multi-label lung disease classification utilizes the CheXpert dataset with over 12,617 images of frontal radiographs. The model integrates RadGraph parsing for efficient annotation extraction, enhancing its ability to accurately classify multiple lung diseases from complex medical images. By achieving an F1 score of 0.69 and an AUROC of 0.86, the proposed model demonstrates potential for clinical applications. Additionally, the paper explores the use of Natural Language Processing (NLP) to parse report metadata and address uncertainties in disease classification, improving the model’s ability to conclusively classify conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to analyze chest X-rays. They used a big dataset of over 12,000 images to train their model, which can recognize different lung diseases like pneumonia and pneumothorax. The model is good at identifying multiple diseases in an image, which can help doctors make more accurate diagnoses. |
Keywords
» Artificial intelligence » Classification » F1 score » Natural language processing » Nlp » Parsing » Transfer learning
