ProGleason-GAN: Conditional progressive growing GAN for prostatic cancer Gleason grade patch synthesis

计算机科学 前列腺癌 样品(材料) 人工智能 公制(单位) 过程(计算) 接收机工作特性 前列腺 模式识别(心理学) 机器学习 癌症 医学 算法 内科学 化学 运营管理 色谱法 经济 操作系统
作者
Alejandro Golfe,Rocío del Amor,Adrián Colomer,María A. Sales,Liria Terrádez,Valery Naranjo
出处
期刊:Computer Methods and Programs in Biomedicine [Elsevier BV]
卷期号:240: 107695-107695 被引量:6
标识
DOI:10.1016/j.cmpb.2023.107695
摘要

Prostate cancer is one of the most common diseases affecting men. The main diagnostic and prognostic reference tool is the Gleason scoring system. An expert pathologist assigns a Gleason grade to a sample of prostate tissue. As this process is very time-consuming, some artificial intelligence applications were developed to automatize it. The training process is often confronted with insufficient and unbalanced databases which affect the generalisability of the models. Therefore, the aim of this work is to develop a generative deep learning model capable of synthesising patches of any selected Gleason grade to perform data augmentation on unbalanced data and test the improvement of classification models.The methodology proposed in this work consists of a conditional Progressive Growing GAN (ProGleason-GAN) capable of synthesising prostate histopathological tissue patches by selecting the desired Gleason Grade cancer pattern in the synthetic sample. The conditional Gleason Grade information is introduced into the model through the embedding layers, so there is no need to add a term to the Wasserstein loss function. We used minibatch standard deviation and pixel normalisation to improve the performance and stability of the training process.The reality assessment of the synthetic samples was performed with the Frechet Inception Distance (FID). We obtained an FID metric of 88.85 for non-cancerous patterns, 81.86 for GG3, 49.32 for GG4 and 108.69 for GG5 after post-processing stain normalisation. In addition, a group of expert pathologists was selected to perform an external validation of the proposed framework. Finally, the application of our proposed framework improved the classification results in SICAPv2 dataset, proving its effectiveness as a data augmentation method.ProGleason-GAN approach combined with a stain normalisation post-processing provides state-of-the-art results regarding Frechet's Inception Distance. This model can synthesise samples of non-cancerous patterns, GG3, GG4 or GG5. The inclusion of conditional information about the Gleason grade during the training process allows the model to select the cancerous pattern in a synthetic sample. The proposed framework can be used as a data augmentation method.
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
无花果应助碧蓝碧凡采纳,获得10
刚刚
_hhhjhhh完成签到,获得积分10
刚刚
Leo完成签到,获得积分10
1秒前
2秒前
淘气科研发布了新的文献求助10
2秒前
小徐801完成签到,获得积分10
2秒前
3秒前
3秒前
wwk完成签到,获得积分10
3秒前
光亮不平完成签到,获得积分10
4秒前
llwxx完成签到,获得积分10
4秒前
梁大海发布了新的文献求助10
4秒前
甜蜜的楷瑞应助_hhhjhhh采纳,获得10
5秒前
諵十一完成签到,获得积分10
5秒前
zqingqing完成签到,获得积分10
6秒前
yize完成签到,获得积分10
7秒前
8秒前
8秒前
崔悦欣发布了新的文献求助10
8秒前
谨慎的擎宇完成签到,获得积分10
9秒前
Thomas完成签到,获得积分10
10秒前
小白鞋完成签到 ,获得积分10
10秒前
maying0318发布了新的文献求助10
12秒前
宣以晴完成签到,获得积分10
12秒前
Nnn完成签到,获得积分10
13秒前
baby完成签到,获得积分10
16秒前
123完成签到,获得积分10
16秒前
17秒前
学术牛马完成签到,获得积分10
17秒前
sl发布了新的文献求助10
19秒前
完美的鹤完成签到,获得积分10
20秒前
hky完成签到,获得积分10
21秒前
TTTHANKS完成签到 ,获得积分10
22秒前
WW完成签到,获得积分10
22秒前
LL完成签到,获得积分10
23秒前
量子星尘发布了新的文献求助10
23秒前
小马的可爱老婆2完成签到,获得积分10
23秒前
缥缈纲完成签到,获得积分10
24秒前
TY完成签到 ,获得积分10
24秒前
迷路的小蚂蚁完成签到,获得积分10
25秒前
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
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots 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 4038426
求助须知:如何正确求助?哪些是违规求助? 3576119
关于积分的说明 11374556
捐赠科研通 3305834
什么是DOI,文献DOI怎么找? 1819339
邀请新用户注册赠送积分活动 892678
科研通“疑难数据库(出版商)”最低求助积分说明 815029