  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
