Leveraging Latents for Efficient Thermography Classification and Segmentation

Tamir Shor, Chaim Baskin, Alexander Bronstein

Keywords: Thermography, Deep Learning, Medical Classification, Medical Segmentation, Breast Cancer

Abstract:

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. Code for reproducing all experiments is available at github.com/tamirshor7/Latents-Guided-Thermography.

Submission Number: 1