  Most applications of deep learning techniques in medical imaging are
supervised and require a large number of labeled data which is expensive and
requires many hours of careful annotation by experts. In this paper, we propose
an unsupervised, physics driven domain specific transporter framework with an
attention mechanism to identify relevant key points with applications in
ultrasound imaging. The proposed framework identifies key points that provide a
concise geometric representation highlighting regions with high structural
variation in ultrasound videos. We incorporate physics driven domain specific
information as a feature probability map and use the radon transform to
highlight features in specific orientations. The proposed framework has been
trained on130 Lung ultrasound (LUS) videos and 113 Wrist ultrasound (WUS)
videos and validated on 100 Lung ultrasound (LUS) videos and 58 Wrist
ultrasound (WUS) videos acquired from multiple centers across the globe. Images
from both datasets were independently assessed by experts to identify
clinically relevant features such as A-lines, B-lines and pleura from LUS and
radial metaphysis, radial epiphysis and carpal bones from WUS videos. The key
points detected from both datasets showed high sensitivity (LUS = 99\% , WUS =
74\%) in detecting the image landmarks identified by experts. Also, on
employing for classification of the given lung image into normal and abnormal
classes, the proposed approach, even with no prior training, achieved an
average accuracy of 97\% and an average F1-score of 95\% respectively on the
task of co-classification with 3 fold cross-validation. With the purely
unsupervised nature of the proposed approach, we expect the key point detection
approach to increase the applicability of ultrasound in various examination
performed in emergency and point of care.
