Fine-grained interactive attention learning for semi-supervised white blood cell classification

计算机科学 人工智能 白细胞 机器学习 支持向量机 模式识别(心理学) 过程(计算) 监督学习 人类血液 标记数据 医学 生理学 人工神经网络 内科学 操作系统
作者
Yan Ha,Zeyu Du,Junfeng Tian
出处
期刊:Biomedical Signal Processing and Control [Elsevier BV]
卷期号:75: 103611-103611 被引量:26
标识
DOI:10.1016/j.bspc.2022.103611
摘要

White blood cell (WBC) is an essential part of the human immune system. To diagnose blood diseases, hematologists have to think about the WBC information. For instance, the number of each type of WBCs often implies the health condition of the human body. Thus, the classification of white blood cell images plays a significant role in the medical diagnosis process. However, manual WBC inspection is time-consuming and labor-intensive for experts, which means automated classification methods are needed for WBC recognition. Another problem is that the traditional automatic recognition system needs a large amount of annotated medical images for training, which is highly costly. In this respect, the semi-supervised learning framework has recently been widely used for medical diagnosis due to its specificity, which can explore relevant information from massive unlabeled data. In this study, a novel semi-supervised white blood cell classification method is proposed, named by Fine-grained Interactive Attention Learning (FIAL). It consists of a Semi-Supervised Teacher-Student (SSTS) module and a Fine-Grained Interactive Attention (FGIA) mechanism. In detail, SSTS employs limited labeled WBC images and generates predicted probability vectors for a large amount of unlabeled WBC samples, like a human. After top-k selection in predicted probabilities, the efficient data can be exploited from unlabeled WBC images for training. With a very small amount of annotated WBC images, FIAL achieves an average accuracy of 93.2% on BCCD dataset when giving 75 labeled images for each category, which sufficiently elaborates our excellent capability on semi-supervised white blood cell image classification task.
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
华仔应助Renesmee采纳,获得10
刚刚
李健应助一只宝贝烊采纳,获得10
刚刚
俊逸易烟发布了新的文献求助10
1秒前
杨123发布了新的文献求助10
1秒前
1秒前
1秒前
ThomasZ完成签到,获得积分10
2秒前
2秒前
Orange应助shifeng采纳,获得10
3秒前
听话的晓夏完成签到,获得积分10
3秒前
乔垣结衣发布了新的文献求助10
3秒前
小豆豆应助wh采纳,获得10
3秒前
89757完成签到,获得积分10
4秒前
yifan92完成签到,获得积分10
4秒前
干红发布了新的文献求助10
4秒前
思源应助Emma采纳,获得30
4秒前
安安爱阎魔完成签到,获得积分10
4秒前
4秒前
TCR完成签到,获得积分10
5秒前
5秒前
sxm完成签到 ,获得积分10
5秒前
SDUMoist完成签到,获得积分10
5秒前
5秒前
Starry完成签到,获得积分10
5秒前
6秒前
sisii完成签到,获得积分10
7秒前
7秒前
7秒前
2718725836发布了新的文献求助10
7秒前
深情安青应助任性的一斩采纳,获得10
8秒前
动听小小完成签到,获得积分10
8秒前
小鲤鱼完成签到,获得积分10
8秒前
8秒前
beikou发布了新的文献求助10
8秒前
8秒前
爆米花应助菠萝贝采纳,获得10
8秒前
9秒前
千千完成签到,获得积分10
9秒前
小二郎应助bb采纳,获得10
10秒前
伶俐皮卡丘完成签到,获得积分10
10秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Immigrant Incorporation in East Asian Democracies 600
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
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 3968771
求助须知:如何正确求助?哪些是违规求助? 3513729
关于积分的说明 11169450
捐赠科研通 3249084
什么是DOI,文献DOI怎么找? 1794592
邀请新用户注册赠送积分活动 875258
科研通“疑难数据库(出版商)”最低求助积分说明 804740