KCC 2021
Current Result Document : 2 / 2
| 한글제목(Korean Title) |
의료 영상에서 비정상 조직을 분할하기 위한 멀티레벨 변환기 기반 분할 네트워크 |
| 영문제목(English Title) |
Multi-Level Transformer-based Segmentation Network for Abnormal Tissue Segmentation in Medical Image |
| 저자(Author) |
두이 푸옹 다오
양형정
김수형국문명
이귀상
강세령
오인재
Duy-Phuong Dao
Hyung-Jeong Yang
Soo-Hyung Kim
Guee-Sang Lee
Sae-Ryung Kang
In-jae Oh
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| 원문수록처(Citation) |
VOL 48 NO. 01 PP. 0700 ~ 0702 (2021. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In medical image, abnormal tissue segmentation is an essential technique for developing healthcare systems, particularly for disease diagnosis and treatment planning. Recently, inspired by advance in deep learning, many architecture networks, designed for segmentation tasks, were introduced and achieved remarkable performance. However, most of those methods are based on convolution operation which only has an ability to capture locality features. In this paper, we propose a Transformer-based segmentation network which can not only capture global context but also integrate multi-level features at encoder branch. The experimental results prove our proposal outperform compared to conventional methods in terms of Dice score. |
| 키워드(Keyword) |
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| 파일첨부 |
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