A Deep Modality-Specific Ensemble for Improving Pneumonia Detection in Chest X-rays.
Rajaraman S, Guo P, Xue Z, Antani SK
Diagnostics (Basel). 2022 Jun 11;12(6). doi: 10.3390/diagnostics12061442. PubMed PMID: 35741252; PubMed Central PMCID: PMC9221627.
Abstract:
Pneumonia is an acute respiratory infectious disease caused by bacteria, fungi, or viruses. Fluid-filled lungs due to the disease result in painful breathing difficulties and reduced oxygen intake. Effective diagnosis is critical for appropriate and timely treatment and improving survival. Chest X-rays (CXRs) are routinely used to screen for the infection. Computer-aided detection methods using conventional deep learning (DL) models for identifying pneumonia-consistent manifestations in CXRs have demonstrated superiority over traditional machine learning approaches. However, their performance is still inadequate to aid in clinical decision-making. This study improves upon the state of the art as follows. Specifically, we train a DL classifier on large collections of CXR images to develop a CXR modality-specific model. Next, we use this model as the classifier backbone in the RetinaNet object detection network. We also initialize this backbone using random weights and ImageNet-pretrained weights. Finally, we construct an ensemble of the best-performing models resulting in improved detection of pneumonia-consistent findings. Experimental results demonstrate that an ensemble of the top-3 performing RetinaNet models outperformed individual models in terms of the mean average precision (mAP) metric (0.3272, 95% CI: (0.3006,0.3538)) toward this task, which is markedly higher than the state of the art (mAP: 0.2547). This performance improvement is attributed to the key modifications in initializing the weights of classifier backbones and constructing model ensembles to reduce prediction variance compared to individual constituent models.
Rajaraman S, Guo P, Xue Z, Antani SK. A Deep Modality-Specific Ensemble for Improving Pneumonia Detection in Chest X-rays.
Diagnostics (Basel). 2022 Jun 11;12(6). doi: 10.3390/diagnostics12061442. PubMed PMID: 35741252; PubMed Central PMCID: PMC9221627.
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