Convolutional autoencoder based model HistoCAE for segmentation of viable tumor regions in liver whole-slide images.

ABSTRACT: Liver cancer is one of the leading causes of cancer deaths in Asia and Africa. It is caused by the Hepatocellular carcinoma (HCC) in almost 90% of all cases. HCC is a malignant tumor and the most common histological type of the primary liver cancers. The detection and evaluation of viable tumor regions in HCC present an important clinical significance since it is a key step to assess response of chemoradiotherapy and tumor cell proportion in genetic tests. Recent advances in computer vision, digital pathology and microscopy imaging enable automatic histopathology image analysis for cancer diagnosis. In this paper, we present a multi-resolution deep learning model HistoCAE for viable tumor segmentation in whole-slide liver histopathology images. We propose convolutional autoencoder (CAE) ba

SUBMITTER: Roy M 

PROVIDER: S-EPMC7794421 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature