Andrea López-Navarrete, Melissa Zimmermann-Vildoso, Vanessa de Brito Poveda, Lilia de Souza Nogueira
Effectiveness of combined virtual and clinical simulation compared with other active teaching strategies on health students' learning: a systematic review protocol
JBI evidence synthesis
rapid development artificial intelligence automated endoscopyassisted diagnostic systems become effective tool reducing diagnostic costs shortening treatment cycle patients typically performance systems depends deep learning models pretrained largescale labeled data example early gastric cancer based endoscopic images however expensive annotation subjectivity annotators lead insufficient classimbalanced endoscopic image dataset datasets detrimental training deep learning models therefore proposed swin transformer encoderbased stylegan stestylegan unbalanced endoscopic image enhancement composed adversarial learning encoder generator firstly pretrained swin transformer introduced encoder extract multiscale features layer layer endoscopic images features subsequently fed mapping block aggregation recombination secondly selfattention mechanism applied generator adds detailed information image layer layer recoded features enabling generator autonomously learn coupling different image regions finally conducted extensive experiments private intestinal metaplasia grading dataset gradea tertiary hospital experimental results show images generated stestylegan closer initial image distribution achieving frchet inception distance fid value 1004 generated images used enhance initial dataset improve robustness classification model achieved top accuracy 86