Weakly Supervised Classification for Nasopharyngeal Carcinoma with Transformer in Whole Slide Images

鼻咽癌 人工智能 计算机科学 模式识别(心理学) 医学 计算机视觉 放射科 放射治疗
作者
Ziwei Hu,Jianchao Wang,Qinquan Gao,Zhida Wu,Hanchuan Xu,Zhechen Guo,Jiawei Quan,Lihua Zhong,Ming Du,Tong Tong,Gang Chen
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:: 1-12 被引量:1
标识
DOI:10.1109/jbhi.2024.3422874
摘要

Pathological examination of nasopharyngeal carcinoma (NPC) is an indispensable factor for diagnosis, guiding clinical treatment and judging prognosis. Traditional and fully supervised NPC diagnosis algorithms require manual delineation of regions of interest on the gigapixel of whole slide images (WSIs), which however is laborious and often biased. In this paper, we propose a weakly supervised framework based on Tokens-to-Token Vision Transformer (WS-T2T-ViT) for accurate NPC classification with only a slide-level label. The label of tile images is inherited from their slide-level label. Specifically, WS-T2T-ViT is composed of the multi-resolution pyramid, T2T-ViT and multi-scale attention module. The multi-resolution pyramid is designed for imitating the coarse-to-fine process of manual pathological analysis to learn features from different magnification levels. The T2T module captures the local and global features to overcome the lack of global information. The multi-scale attention module improves classification performance by weighting the contributions of different granularity levels. Extensive experiments are performed on the 802-patient NPC and CAMELYON16 dataset. WS-T2T-ViT achieves an area under the receiver operating characteristic curve (AUC) of 0.989 for NPC classification on the NPC dataset. The experiment results of CAMELYON16 dataset demonstrate the robustness and generalizability of WS-T2T-ViT in WSI-level classification.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
独角兽完成签到,获得积分10
刚刚
1秒前
NexusExplorer应助儒雅致远采纳,获得10
1秒前
chlgkmoney完成签到 ,获得积分20
1秒前
1秒前
2秒前
852应助科研通管家采纳,获得10
2秒前
wanci应助科研通管家采纳,获得10
2秒前
1111应助科研通管家采纳,获得20
2秒前
快乐的胖子应助科研通管家采纳,获得100
2秒前
2秒前
JamesPei应助科研通管家采纳,获得10
2秒前
skyer应助科研通管家采纳,获得10
2秒前
褪黑素应助科研通管家采纳,获得10
2秒前
CipherSage应助科研通管家采纳,获得10
2秒前
czh应助科研通管家采纳,获得10
2秒前
3秒前
星辰大海应助科研通管家采纳,获得10
3秒前
桐桐应助科研通管家采纳,获得10
3秒前
CodeCraft应助科研通管家采纳,获得10
3秒前
3秒前
天天快乐应助科研通管家采纳,获得10
3秒前
SYLH应助科研通管家采纳,获得20
3秒前
领导范儿应助科研通管家采纳,获得10
3秒前
3秒前
传奇3应助科研通管家采纳,获得10
3秒前
Orange应助科研通管家采纳,获得10
3秒前
情怀应助诚心凌珍采纳,获得10
3秒前
顾矜应助科研通管家采纳,获得10
3秒前
小二郎应助科研通管家采纳,获得10
3秒前
卡卡西应助科研通管家采纳,获得30
4秒前
今后应助科研通管家采纳,获得10
4秒前
4秒前
4秒前
Orange应助科研通管家采纳,获得10
4秒前
星辰大海应助科研通管家采纳,获得10
4秒前
4秒前
4秒前
CAOHOU应助科研通管家采纳,获得10
4秒前
4秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Economic Geography and Public Policy 900
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988786
求助须知:如何正确求助?哪些是违规求助? 3531116
关于积分的说明 11252493
捐赠科研通 3269766
什么是DOI,文献DOI怎么找? 1804771
邀请新用户注册赠送积分活动 881870
科研通“疑难数据库(出版商)”最低求助积分说明 809021