Weakly Supervised Learning using Attention gates for colon cancer histopathological image segmentation

计算机科学 人工智能 分割 深度学习 稳健性(进化) 机器学习 数字化病理学 模式识别(心理学) 过程(计算) 人工神经网络 生物化学 基因 操作系统 化学
作者
Ahmed Ben Hamida,Maxime Devanne,Jonathan Weber,Caroline Truntzer,Valentin Dérangère,François Ghiringhelli,Germain Forestier,Cédric Wemmert
出处
期刊:Artificial Intelligence in Medicine [Elsevier BV]
卷期号:133: 102407-102407 被引量:8
标识
DOI:10.1016/j.artmed.2022.102407
摘要

Recently, Artificial Intelligence namely Deep Learning methods have revolutionized a wide range of domains and applications. Besides, Digital Pathology has so far played a major role in the diagnosis and the prognosis of tumors. However, the characteristics of the Whole Slide Images namely the gigapixel size, high resolution and the shortage of richly labeled samples have hindered the efficiency of classical Machine Learning methods. That goes without saying that traditional methods are poor in generalization to different tasks and data contents. Regarding the success of Deep learning when dealing with Large Scale applications, we have resorted to the use of such models for histopathological image segmentation tasks. First, we review and compare the classical UNet and Att-UNet models for colon cancer WSI segmentation in a sparsely annotated data scenario. Then, we introduce novel enhanced models of the Att-UNet where different schemes are proposed for the skip connections and spatial attention gates positions in the network. In fact, spatial attention gates assist the training process and enable the model to avoid irrelevant feature learning. Alternating the presence of such modules namely in our Alter-AttUNet model adds robustness and ensures better image segmentation results. In order to cope with the lack of richly annotated data in our AiCOLO colon cancer dataset, we suggest the use of a multi-step training strategy that also deals with the WSI sparse annotations and unbalanced class issues. All proposed methods outperform state-of-the-art approaches but Alter-AttUNet generates the best compromise between accurate results and light network. The model achieves 95.88% accuracy with our sparse AiCOLO colon cancer datasets. Finally, to evaluate and validate our proposed architectures we resort to publicly available WSI data: the NCT-CRC-HE-100K, the CRC-5000 and the Warwick colon cancer histopathological dataset. Respective accuracies of 99.65%, 99.73% and 79.03% were reached. A comparison with state-of-art approaches is established to view and compare the key solutions for histopathological image segmentation.
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
hellokitty完成签到,获得积分10
1秒前
1秒前
小四发布了新的文献求助10
2秒前
8秒前
西瓜完成签到 ,获得积分10
8秒前
包容的忆灵完成签到 ,获得积分10
11秒前
高兴尔冬发布了新的文献求助10
13秒前
xiang完成签到 ,获得积分0
16秒前
小四完成签到,获得积分10
19秒前
FashionBoy应助slayers采纳,获得30
25秒前
量子星尘发布了新的文献求助10
28秒前
黑眼圈完成签到 ,获得积分10
34秒前
jia完成签到 ,获得积分10
35秒前
如履平川完成签到 ,获得积分10
36秒前
科目三应助忧伤的步美采纳,获得10
37秒前
大椒完成签到 ,获得积分10
40秒前
43秒前
45秒前
wisdom完成签到,获得积分10
45秒前
slayers发布了新的文献求助30
48秒前
49秒前
e746700020完成签到,获得积分10
50秒前
高兴尔冬完成签到,获得积分10
50秒前
李爱国应助不安的秋白采纳,获得10
52秒前
忧伤的步美完成签到,获得积分10
57秒前
小西完成签到 ,获得积分10
58秒前
郝老头完成签到,获得积分10
59秒前
13313完成签到,获得积分10
1分钟前
su完成签到 ,获得积分10
1分钟前
1分钟前
1分钟前
量子星尘发布了新的文献求助10
1分钟前
slayers完成签到 ,获得积分10
1分钟前
1分钟前
知犯何逆完成签到,获得积分10
1分钟前
Krsky完成签到,获得积分10
1分钟前
ding应助不安的秋白采纳,获得10
1分钟前
1分钟前
1分钟前
HHHAN发布了新的文献求助10
1分钟前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
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 4038039
求助须知:如何正确求助?哪些是违规求助? 3575756
关于积分的说明 11373782
捐赠科研通 3305574
什么是DOI,文献DOI怎么找? 1819239
邀请新用户注册赠送积分活动 892655
科研通“疑难数据库(出版商)”最低求助积分说明 815022