Ultrasound Image-Based Diagnosis of Cirrhosis with an End-to-End Deep Learning model

Cirrhosis is a chronic liver disease that seriously jeopardizes the life and health of patients. Currently, ultrasound (US) imaging is commonly used by the computer-aided diagnosis (CAD) system to diagnose cirrhosis. With the rapid development of artificial intelligence, deep learning methods for cirrhosis diagnosis using ultrasound image data have emerged. However, due to US images' complexity and variability, this input usually requires manual annotation. This study proposes LiverTL, an end-to-end deep learning approach for the automatic cirrhosis ultrasound image classification to overcome these limitations. LiverTL includes an automatic region of interest (ROI) detection module to support various ultrasound images' ROI extraction. Simultaneously, the classification module utilizes ROI areas and obtain the cirrhosis diagnosis results through the transfer learning network. We find that LiverTL achieves high classification accuracy on our evaluation data set. The cirrhosis data experiments suggest that a proper pre-training model for transfer learning is crucial for the classification results. These findings potentially pave the way to advance the diagnosis and therapy of cirrhosis.