CrossEAI: Using Explainable AI to generate better bounding boxes for Chest X-ray images
This work addresses a critical need in healthcare AI for better interpretability and accuracy in medical imaging diagnosis, specifically for chest X-rays, though it is incremental as it builds on existing explainable AI techniques.
The paper tackles the problem of generating accurate bounding boxes for chest X-ray images, where existing methods often produce boxes larger than ground truth and include non-disease areas, by proposing CrossEAI, which combines heatmap and gradient maps from explainable AI methods to create more targeted boxes, resulting in a 9% improvement in average IoU over state-of-the-art models.
Explainability is critical for deep learning applications in healthcare which are mandated to provide interpretations to both patients and doctors according to legal regulations and responsibilities. Explainable AI methods, such as feature importance using integrated gradients, model approximation using LIME, or neuron activation and layer conductance to provide interpretations for certain health risk predictions. In medical imaging diagnosis, disease classification usually achieves high accuracy, but generated bounding boxes have much lower Intersection over Union (IoU). Different methods with self-supervised or semi-supervised learning strategies have been proposed, but few improvements have been identified for bounding box generation. Previous work shows that bounding boxes generated by these methods are usually larger than ground truth and contain major non-disease area. This paper utilizes the advantages of post-hoc AI explainable methods to generate bounding boxes for chest x-ray image diagnosis. In this work, we propose CrossEAI which combines heatmap and gradient map to generate more targeted bounding boxes. By using weighted average of Guided Backpropagation and Grad-CAM++, we are able to generate bounding boxes which are closer to the ground truth. We evaluate our model on a chest x-ray dataset. The performance has significant improvement over the state of the art model with the same setting, with $9\%$ improvement in average of all diseases over all IoU. Moreover, as a model that does not use any ground truth bounding box information for training, we achieve same performance in general as the model that uses $80\%$ of the ground truth bounding box information for training