“…All models were developed with TensorFlow 1.10.0 and Keras 2.2.4 [ 36 ] on a server with four TITAN XP GPUs. Because the dataset is imbalanced, we adopted class weights to avoid the biased towards the majority class [ 44 ]. Fig.…”
“…All models were developed with TensorFlow 1.10.0 and Keras 2.2.4 [ 36 ] on a server with four TITAN XP GPUs. Because the dataset is imbalanced, we adopted class weights to avoid the biased towards the majority class [ 44 ]. Fig.…”
“…The optimal model in internal cross validation will be used to test with prospective validation dataset. We studied on the effect of data augmentation 20,21 on classification performance, the results show data augmentation cannot improve the performance significantly 22 . Besides, we hope study and compare the effectiveness of DAAN, CNN, and ViT without any interference, Therefore, we did not adopt data augmentation.…”
Section: Methodsmentioning
confidence: 99%
“…We studied on the effect of data augmentation 20,21 on classification performance, the results show data augmentation cannot improve the performance significantly. 22 Besides, we hope study and compare the effectiveness of DAAN, CNN, and ViT without any interference, Therefore, we did not adopt data augmentation. All models were developed with PyTorch 1.8 on the server with four NVIDIA RTX A4000 GPUs (Graphical Processing Units).…”
Section: Development Of Deep Learning Modelsmentioning
Quality controlling for capsule endoscopic images can be completed with the assistance of artificial intelligence, but the labeling process is time‐consuming. Domain adaption is a robust tool for cross‐domain learning to reach a consistent target. Current research aims to study the feasibility and effectiveness of domain adaption from ordinary endoscopic images to capsule endoscopic images in quality controlling. Dynamic adversarial adaptation network (DAAN) was trained to identify low‐quality images using ordinary endoscopic images with corresponding labels (source domain with supervision) and capsule endoscopic images without corresponding labels (target domain without supervision) so that image quality controlling can be transferred from ordinary to capsule endoscopic images. 62,850 images from capsule endoscopy and 17,434 images from ordinary endoscopy were included in developing deep learning models. In internal cross‐validation, DAAN achieved an average area under receiver operating characteristic curve (AUROC) of 0.8638 (95% confidence interval [CI] 0.6753–1.0000) in filtering low‐quality images for capsule endoscopic images, compared with CNN B/16 and L/32, which were also trained with ordinary endoscopic images with corresponding labels. 18,636 images from 355 patients who received capsule endoscopy were prospectively collected. The AUROC of DAAN reached 0.9471 (95% CI 0.9428–0.9511), which surpassed CNN (0.8570 and 95% CI [0.8529–0.8608]) and ViT (L/32: 0.8183 and 95% CI [0.8143–0.8220] and B/16: 0.7779 and 95% CI [0.7960–0.8036]). Domain adaption can complete image quality controlling task in capsule endoscopic images with the supervision of ordinary endoscopic images, whose quantity is smaller so that the annotation workload can be alleviated.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
