Improving Idiopathic Pulmonary Fibrosis Damage Prediction with Segmented Images in a Deep Learning Model

This work introduces a semantic segmentation model, UNet, as a preprocessing module to an algorithm predicting lung damage caused by Idiopathic Pulmonary Fibrosis. By modifying the model input through the incorporation of a guide image (a segmentation result) into the original image, we observed an improved performance of eight out of twelve tested backbones in the prediction model, with an improvement of up to 0.57 in the LLLm metric. This study underscores the significance of data preprocessing in deep learning models’ performance. The inclusion of additional data, such as segmented images, can significantly enhance a model’s ability to perform specific tasks, emphasizing the need for careful data preprocessing to obtain precise and reliable results when implementing deep learning models for lung damage prediction.