Pneumoperitoneum Detection In Chest X-Ray By A Deep Learning Ensemble With Model Explainability
| Autor: | David L. Richmond, Maria V. Sainz de Cea, David Gruen |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Deep learning 030208 emergency & critical care medicine Pattern recognition medicine.disease 030218 nuclear medicine & medical imaging Data modeling body regions 03 medical and health sciences 0302 clinical medicine Pneumoperitoneum Emergency surgery medicine Artificial intelligence business |
| Zdroj: | ISBI |
| DOI: | 10.1109/isbi48211.2021.9434122 |
| Popis: | Pneumoperitoneum (free air in the peritoneal cavity) is a rare condition that can be life threatening and require emergency surgery. It can be detected in chest X-ray but there are some challenges associated to this detection, such as small amounts of air that may be missed by a radiologist, or pseudo-pneumoperitoneum (air in the abdomen that may look like pneumoperitoneum). In this work, we propose using an ensemble of deep learning models trained on different subsets of data to boost the classification and generalization performance of the model as well as hard-negative mining to mitigate the effect of pseudo-pneumoperitoneum. We demonstrate superior performance when the model ensemble is utilized as well as good localization of the finding with multiple model explainability techniques. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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