Cell classification with worse-case boosting for intelligent cervical cancer screening

Boosting(机器学习) 分类器(UML) 概化理论 人工智能 计算机科学 机器学习 训练集 梯度升压 宫颈癌 模式识别(心理学) 医学 数学 统计 癌症 随机森林 内科学
作者
Youyi Song,Jing Zou,Kup‐Sze Choi,Baiying Lei,Jing Qin
出处
期刊:Medical Image Analysis [Elsevier]
卷期号:91: 103014-103014
标识
DOI:10.1016/j.media.2023.103014
摘要

Cell classification underpins intelligent cervical cancer screening, a cytology examination that effectively decreases both the morbidity and mortality of cervical cancer. This task, however, is rather challenging, mainly due to the difficulty of collecting a training dataset representative sufficiently of the unseen test data, as there are wide variations of cells' appearance and shape at different cancerous statuses. This difficulty makes the classifier, though trained properly, often classify wrongly for cells that are underrepresented by the training dataset, eventually leading to a wrong screening result. To address it, we propose a new learning algorithm, called worse-case boosting, for classifiers effectively learning from under-representative datasets in cervical cell classification. The key idea is to learn more from worse-case data for which the classifier has a larger gradient norm compared to other training data, so these data are more likely to correspond to underrepresented data, by dynamically assigning them more training iterations and larger loss weights for boosting the generalizability of the classifier on underrepresented data. We achieve this idea by sampling worse-case data per the gradient norm information and then enhancing their loss values to update the classifier. We demonstrate the effectiveness of this new learning algorithm on two publicly available cervical cell classification datasets (the two largest ones to the best of our knowledge), and positive results (4% accuracy improvement) yield in the extensive experiments. The source codes are available at: https://github.com/YouyiSong/Worse-Case-Boosting.
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
大幅提高文件上传限制,最高150M (2024-4-1)

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
慕青应助zzzz采纳,获得10
刚刚
chichenglin发布了新的文献求助10
刚刚
刚刚
Efei完成签到,获得积分10
刚刚
李李完成签到,获得积分10
1秒前
今后应助PONY采纳,获得10
1秒前
Anoxia完成签到,获得积分10
1秒前
Sene完成签到,获得积分10
2秒前
摆烂的鲲完成签到,获得积分10
3秒前
3秒前
YANG完成签到 ,获得积分10
4秒前
Rez完成签到,获得积分10
4秒前
Anoxia发布了新的文献求助10
4秒前
听白完成签到 ,获得积分10
4秒前
zhinian28完成签到,获得积分10
4秒前
gossie完成签到,获得积分10
4秒前
5秒前
万能图书馆应助缥缈傥采纳,获得10
5秒前
joysa完成签到,获得积分10
6秒前
6秒前
Yvan完成签到,获得积分10
6秒前
优美怀蕊完成签到,获得积分10
7秒前
顾矜应助凄凉山谷的风采纳,获得10
8秒前
11完成签到,获得积分10
8秒前
包容胡萝卜完成签到,获得积分10
8秒前
BUG完成签到,获得积分10
9秒前
明理的问柳完成签到 ,获得积分10
9秒前
爆米花应助兴奋芷采纳,获得10
9秒前
Lqiqiqi完成签到,获得积分10
9秒前
彭于晏应助IAMXC采纳,获得10
10秒前
10秒前
颖颖子完成签到,获得积分10
10秒前
嘟嘟请让一让完成签到,获得积分10
11秒前
孔雀翎发布了新的文献求助10
11秒前
001完成签到 ,获得积分10
11秒前
jiaozhiping完成签到,获得积分10
12秒前
子寒完成签到,获得积分10
12秒前
cxzhao完成签到,获得积分10
13秒前
13秒前
慕青应助柑橘采纳,获得10
14秒前
高分求助中
Evolution 10000
Sustainability in Tides Chemistry 2800
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
юрские динозавры восточного забайкалья 800
English Wealden Fossils 700
Foreign Policy of the French Second Empire: A Bibliography 500
Chen Hansheng: China’s Last Romantic Revolutionary 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 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 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3147236
求助须知:如何正确求助?哪些是违规求助? 2798534
关于积分的说明 7829576
捐赠科研通 2455246
什么是DOI,文献DOI怎么找? 1306655
科研通“疑难数据库(出版商)”最低求助积分说明 627883
版权声明 601567