Leveraging Latents for Efficient Thermography Classification and
  Segmentation
Tamir Shor Chaim Baskin Alex Bronstein
Breast cancer is a prominent health concern worldwide, currently being the
secondmost common and second-deadliest type of cancer in women. While current
breast cancer diagnosis mainly relies on mammography imaging, in recent years
the use of thermography for breast cancer imaging has been garnering growing
popularity. Thermographic imaging relies on infrared cameras to capture
body-emitted heat distributions. While these heat signatures have proven useful
for computer-vision systems for accurate breast cancer segmentation and
classification, prior work often relies on handcrafted feature engineering or
complex architectures, potentially limiting the comparability and applicability
of these methods. In this work, we present a novel algorithm for both breast
cancer classification and segmentation. Rather than focusing efforts on manual
feature and architecture engineering, our algorithm focuses on leveraging an
informative, learned feature space, thus making our solution simpler to use and
extend to other frameworks and downstream tasks, as well as more applicable to
data-scarce settings. Our classification produces SOTA results, while we are
the first work to produce segmentation regions studied in this paper.