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Chest Radiographs in Congestive Heart Failure: Visualizing Neural Network Learning
Jarrel C Y Seah1, Jennifer S N Tang1, Andy Kitchen1
1From the Department of Radiology, Alfred Hospital, 55 Commercial Rd, Melbourne, Victoria 3004, Australia (J.C.Y.S., A.F.D.); Department of Radiology, Royal Melbourne Hospital, Melbourne, Australia (J.S.N.T., F.G.); Melbourne, Australia (A.K.); and Department of Radiology, Melbourne University, Melbourne, Australia (F.G.).
Radiology
|November 7, 2018
Summary
Generative Visual Rationales (GVRs) effectively visualize neural network learning of chest radiograph features for congestive heart failure (CHF). This method helps identify bias and overfitted models in AI-driven medical imaging analysis.
Area of Science:
- Artificial Intelligence in Medical Imaging
- Radiology and Diagnostic Imaging
- Cardiology and Cardiovascular Diseases
Background:
- Congestive heart failure (CHF) diagnosis often relies on chest radiograph interpretation.
- Neural networks show promise in analyzing medical images but require transparent learning processes.
- Visualizing AI learning is crucial for validating diagnostic accuracy and identifying model biases.
Purpose of the Study:
- To evaluate Generative Visual Rationales (GVRs) for visualizing neural network learning of chest radiograph features indicative of CHF.
- To assess the ability of GVRs to differentiate between correctly trained and overfitted AI models in CHF detection.
- To enable the identification of potential biases in AI models through visual interpretation of learned features.
Main Methods:
- A generative model was trained on a large dataset of chest radiographs (103,489 images from 46,712 patients).
- A neural network was trained to estimate B-type natriuretic peptide (BNP) levels, a marker for CHF, using encoded radiograph representations.
- Generative Visual Rationales (GVRs) were generated to visualize 'healthy' radiographs based on predicted BNP levels, and compared between correctly trained and overfitted models.
Main Results:
- The correctly trained model achieved an Area Under the Curve (AUC) of 0.82 for CHF detection at a BNP cutoff of 100 ng/L.
- GVRs from the correctly trained model more frequently highlighted conventional CHF features like cardiomegaly and pleural effusions compared to the overfitted model.
- Expert assessment confirmed that GVRs effectively distinguished between accurate and biased model learning.
Conclusions:
- Generative Visual Rationales (GVRs) are a valuable tool for understanding neural network learning of CHF features on chest radiographs.
- GVRs facilitate the detection of bias and overfitted models in AI-based medical image analysis.
- This visualization technique enhances the interpretability and trustworthiness of AI in diagnosing conditions like CHF.
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