Attention Mechanism-Enhanced Deep Learning for the Differential Diagnosis of Idiopathic Pulmonary Fibrosis on Computed Tomography.
TL;DR
Based solely on radiological images, the attention-enhanced VGG-16 model achieved high accuracy in the differential diagnosis of IPF, and present a potential approach that could be used in clinical decision support systems.
Abstract
INTRODUCTION/
Objective
Many other diseases can produce a similar pattern; therefore, the diagnosis of IPF is made by exclusion, based on the absence of alternative pathologies. To differentiate IPF from other pathologies, the study developed a deep learning model with an attention mechanism to improve performance.
Methods
This retrospective, single-center study included 96 patients (46 IPF and 50 non-IPF) with a typical UIP pattern on HRCT. The data were split at the patient level into training (70%), validation (15%), and test (15%) sets using stratified randomization (seed = 42). Squeeze-and-Excitation (SE) blocks were incorporated after each convolutional stage of VGG-16 to enable channel-wise feature recalibration. The model was trained using the AdamW optimizer (learning rate = 1×10-4, batch size = 32, 50 epochs, early stopping with a patience of 7), with augmentation applied only to the training set. Test predictions were generated from original, unaugmented slices, and patient-level labels were determined via majority voting. Gradient-weighted Class Activation Mapping (Grad-CAM) was applied post hoc for heatmap visualization. Model performance was evaluated using accuracy, sensitivity, specificity, F1-score, Area Under the Curve (AUC), and Cohen's kappa.
Results
The SE-VGG-16 model achieved a patient-level accuracy of 87.2%, sensitivity 86.1%, specificity 88.4%, F1-score 0.870, and AUC 0.91. Ablation studies demonstrated a 4.1% accuracy improvement over baseline VGG-16. Grad-CAM heatmaps consistently highlighted subpleural reticular opacities and honeycombing regions, aligning with established radiological criteria.
Discussion
The study has successfully distinguished IPF from other interstitial diseases with a high degree of accuracy. Despite the existence of numerous studies in the literature on the differential diagnosis of IPF, research on deep learning and studies combining deep learning with attention maps are quite limited. Unlike traditional deep learning models, the use of an attention mechanism has enabled the model to focus on pathological regions, thereby producing more reliable results. The findings present a potential approach that could be used in clinical decision support systems.
Conclusion
Based solely on radiological images, the attention-enhanced VGG-16 model achieved high accuracy in the differential diagnosis of IPF.