Leveraging Latents for Efficient Thermography Classification and Segmentation

TL;DR

This work presents a novel algorithm for both breast cancer classification and segmentation that focuses on leveraging an informative, learned feature space, thus making the solution simpler to use and extend to other frameworks and downstream tasks, as well as more applicable to data-scarce settings.

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.

Figures (1)

• Figure 1: Qualitative Segmentation Results for best-performing model(grayscale encoder /w heatmap decoder), achieving segmentation similar to ground truth.