Intranet: infrared-based transformers for 2D medical image segmentation

Infrared (IR) spectroscopic imaging is widely employed in medical imaging applications due to its ability to capture both chemical and spatial information of biological tissues. In recent years, convolutional neural networks (CNNs), including the well-known U-Net model, have demonstrated impressive performance in biomedical image segmentation. However, the inherent locality of convolution limits the effectiveness of these models for encoding IR data, resulting in suboptimal performance in for some applications. In this work we propose an infrared-based transformer network named INTRANET for IR image segmentation. This novel model leverages the strength of the transformer encoders to segment infrared colon images effectively. Incorporating the skip-connection and transformer encoders, INTRANET overcomes the issue of pure convolution models, such as the difficulty of capturing long-range dependencies. We train several encoder-decoder models on a colon dataset of IR images to evaluate the existing convolution models and our proposed method. Our model achieves an AUC score of 0.9872, using 17 spectral bands for the segmentation task. Experimental results demonstrate that INTRANET significantly improves over the pure convolution models, especially when the input IR band number is limited.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2023

Author dc:creator

• Lin, Hangzheng

Contributors dc:contributor

• Kindratenko, Volodymyr

• Machine Learning
• Biomedical
• Infrared Imaging
• Segmentation
• Transformer
• Deep Learning

Statement dc:rights

• Copyright 2023 Hangzheng Lin

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/120132

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F120132