COMPARISON OF DEEP NEURAL  NETWORK ARCHITECTURES FOR  ULTRASOUND FAT-FRACTION  ESTIMATION IN NON-ALCOHOLIC FATTY  LIVER DISEASE

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https://hdl.handle.net/2142/114978

Title: COMPARISON OF DEEP NEURAL NETWORK ARCHITECTURES FOR ULTRASOUND FAT-FRACTION ESTIMATION IN NON-ALCOHOLIC FATTY LIVER DISEASE
Author(s): Cai, William
Issue Date: 2022-05
Keyword(s): deep learning; transformer; ultrasound; nonalcoholic fatty liver disease
Abstract: Nonalcoholic fatty liver disease (NAFLD) is the most common form of chronic liver disease, affecting approximately one quarter of the United States’ population. With traditional methods of diagnosis such as biopsies being both expensive and invasive, ultrasound imaging for NAFLD is becoming increasingly popular. Deep learning models based on ultrasound radiofrequency (RF) signals have shown promising results in medical applications such as non-invasively quantifying the amount of fat in a human liver. A one-dimensional convolutional neural network (1D-CNN) was recently proposed to estimate the liver fat content from ultrasound RF signals. In this thesis, we compare the performance between the 1D-CNN and the popular transformer architecture for this task. The transformer model is often used in the fields of natural language processing and computer vision due to its self-attention mechanism, and it outperforms many other models. The comparison between the 1D-CNN and transformer will provide insights into architecture selection for deep learning using ultrasound RF signals.
Type of Resource: text
Language: en
Handle URL: https://hdl.handle.net/2142/114978

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