计算机与现代化Issue(4)：43-47,98,6.DOI:10.3969/j.issn.1006-2475.2024.04.008
基于Ghost卷积的高级别浆液性卵巢癌复发预测方法
Ghost Convolution Based Prediction Method for Recurrence of High Grade Serous Ovarian Cancer
摘要
Abstract
High grade serous ovarian cancer is a malignant tumor disease,and preoperative recurrence prediction can help clini-cal doctors provide personalized treatment plans for patients and reduce the mortality rate.Due to the less and difficult-to-obtain medical data of this disease,its deep learning model is difficult to obtain sufficient training,and the accuracy of recurrence pre-diction needs to be improved.To address this issue,this article proposes an improved low-parameter residual network TGE-ResNet34,which uses ResNet34 as the backbone network and replaces traditional convolution modules with Ghost convolutions to extract lesion area features and reduce the model's parameter volume.The ECA(Efficient Channel Attention)attention mechanism is incorporated between two Ghost convolutions to suppress interference from useless feature extraction.Finally,the model is evaluated through a five-fold cross-validation to avoid the randomness of data partitioning.The experimental results show that the accuracy of the improved TGE-ResNet34 network is 96.01%,which is 4.52 percentage points higher than the origi-nal baseline network's accuracy and reduces the parameter volume by 15.98 M.关键词
高级别浆液性卵巢癌/残差网络/Ghost卷积/注意力Key words
high grade serous ovarian cancer/residual network/Ghost convolution/attention分类
信息技术与安全科学引用本文复制引用
唐艺菠,崔少国,万皓明,王锐,刘丽丽..基于Ghost卷积的高级别浆液性卵巢癌复发预测方法[J].计算机与现代化,2024,(4):43-47,98,6.基金项目
国家自然科学基金资助项目(62003065) （62003065）
重庆市科技局自然科学基金资助项目(2022NSCQ-MSX2933,2022TFII-OFX0262,cstc2019jscx-mbdxX0061) （2022NSCQ-MSX2933,2022TFII-OFX0262,cstc2019jscx-mbdxX0061）
教育部人文社科规划基金资助项目(22YJA870005) （22YJA870005）
重庆市教委重点项目(KJZD-K202200510) （KJZD-K202200510）
重庆市社会科学规划项目(2022NDYB119) （2022NDYB119）
重庆师范大学人才基金资助项目(20XLB004) （20XLB004）
重庆市研究生科研创新项目(CYS22558,CYS22555) （CYS22558,CYS22555）
重庆师范大学研究生科研创新项目(YKC22019) （YKC22019）
