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

Cai, William

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https://hdl.handle.net/2142/124893 Copy

Description

Title

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

Author(s)

Cai, William

Issue Date

2022-05-01

Keyword(s)

deep learning; transformer; ultrasound; nonalcoholic fatty liver disease

Date of Ingest

2024-10-15T15:20:00-05:00

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

Genre of Resource

dissertation/thesis

Language

eng

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