  We propose using a pre-trained segmentation model to perform diagnostic
classification in order to achieve better generalization and interpretability,
terming the technique reverse-transfer learning. We present an architecture to
convert segmentation models to classification models. We compare and contrast
dense vs sparse segmentation labeling and study its impact on diagnostic
classification. We compare the performance of U-Net trained with dense and
sparse labels to segment A-lines, B-lines, and Pleural lines on a custom
dataset of lung ultrasound scans from 4 patients. Our experiments show that
dense labels help reduce false positive detection. We study the classification
capability of the dense and sparse trained U-Net and contrast it with a
non-pretrained U-Net, to detect and differentiate COVID-19 and Pneumonia on a
large ultrasound dataset of about 40k curvilinear and linear probe images. Our
segmentation-based models perform better classification when using pretrained
segmentation weights, with the dense-label pretrained U-Net performing the
best.
