2020/08/07 by Bowen Li, Li, Bowen, Ke Yan +11
Biochemistry, Genetics and Molecular Biology · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Hepatocellular Carcinoma Treatment and Prognosis #Image and Video Processing (eess.IV) #Liver Disease Diagnosis and Treatment #Machine Learning (cs.LG) #Spectroscopy Techniques in Biomedical and Chemical Research #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2008.03352
openalex publication_date 2020/08/07 · openalex created_date 2020/08/13 · openalex updated_date 2026/07/28
Ultrasound (US) is a critical modality for diagnosing liver fibrosis. Unfortunately, assessment is very subjective, motivating automated approaches. We introduce a principled deep convolutional neural network (CNN) workflow that incorporates several innovations. First, to avoid overfitting on non-relevant image features, we force the network to focus on a clinical region of interest (ROI), encompassing the liver parenchyma and upper border. Second, we introduce global heteroimage fusion (GHIF), which allows the CNN to fuse features from any arbitrary number of images in a study, increasing its versatility and flexibility. Finally, we use 'style'-based view-specific parameterization (VSP) to tailor the CNN processing for different viewpoints of the liver, while keeping the majority of parameters the same across views. Experiments on a dataset of 610 patient studies (6979 images) demonstrate that our pipeline can contribute roughly 7% and 22% improvements in partial area under the curve and recall at 90% precision, respectively, over conventional classifiers, validating our approach to this crucial problem.