Self-Supervised Transformer-Based Pipeline for Liver Tumor Segmentation and Type Classification.

ABSTRACT:

Purpose

It is essential to detect and segment liver tumors to guide treatment and track disease progression. To reduce the need for large annotated data sets, we present an end-to-end pipeline that uses self-supervised pretraining to improve segmentation and then classifies tumor types with a separate pretrained classifier applied to the segmented tumor regions.

Methods

First, we pretrained the encoder of a transformer-based network using a self-supervised approach on unlabeled abdominal computed tomography images. Subsequently, we fine-tuned the segmentation network to segment the liver and tumors, and the tumor regions were classified using a pretrained convolutional neural network (Inception-v3 architecture) as intrahepatic cholangiocarcinoma (ICC), hepatocellular carcino

SUBMITTER: Mojtahedi R 

PROVIDER: S-EPMC12866948 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature