Attention model improves liver cancer type classification and localization
Attention-guided classification and tumor localization of combined hepatocellular-cholangiocarcinoma
Computer Vision and Pattern Recognition
Summary
Distinguishing between types of liver cancer can be tricky because some tumors contain mixed features of two cancers. The authors developed a computer model that looks at detailed microscope images to tell these cancer types apart and can highlight where each cancer type is located in the images. This helps doctors better recognize complex liver cancers. Improving this identification can be important because different cancers need different treatments.
What this means in practice
- •For pathology labs: Automatically classify liver tumor types and locate different cancer components within microscope slides to assist diagnosis.
- •For medical imaging software teams: Integrate attention-guided classification models to enhance tools that analyze histology images for complex liver cancers.
Authors
Xiaowen Liang, Hélène Cazier, Aurélie Beaufrère, Miguel Albuquerque, Valérie Paradis, Cédric Wemmert
Abstract
Combined hepatocellular-cholangiocarcinomas (cHCC-CCA) are a type of cancer that share components of two other primary liver cancers: hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA). Accurate diagnosis of cHCC-CCA, of which the behavior may resemble that of either HCC or CCA, remains a challenge due to strong intratumoral heterogeneity. Since the prognoses differ between liver cancer types, it is necessary to improve their morphological characterization and recognition in order to accurately identify cHCC-CCA. In this work, we use whole slide images (WSIs) of the two main types of liver cancer (HCC and CCA) and cHCC-CCA, stained with Hematoxylin-Eosin-Safran (HES), to train a classification model that can distinguish between these subtypes and provides a potential solution for localizing different types of tumors in the combined liver cancer images.