Yuanyuan Zheng, Xiaoyu Niu, Qian Wei, Yijing Li, Lifeng Li, Jie Zhao
Familial Esophageal Cancer in Taihang Mountain, China: An Era of Personalized Medicine Based on Family and Population Perspective
Cell transplantation
lung cancer seriously threatened human health due high lethality morbidity lung adenocarcinoma particular one common subtypes lung cancer pathological diagnosis regarded gold standard cancer diagnosis however traditional manual screening lung cancer pathology images time consuming error prone computeraided diagnostic systems emerged solve problem current research methods unable fully exploit beneficial features inherent within patches characterized high model complexity significant computational effort study deep learning framework called multiscale network msnet proposed automatic detection lung adenocarcinoma pathology images msnet designed efficiently harness valuable features within data patches simultaneously reducing model complexity computational demands storage space requirements msnet framework employs dual data stream input method input method msnet combines swin transformer mlpmixer models address global information patches local information within patch subsequently msnet uses multilayer perceptron mlp module fuse local global features perform classification output final detection results addition dataset lung adenocarcinoma pathology images containing three categories created training testing msnet framework experimental results show diagnostic accuracy msnet lung adenocarcinoma pathology images 9655 summary msnet high classification performance shows effectiveness potential classification lung adenocarcinoma pathology images