Primary investigation of deep learning models for japanese “ group classification ” of whole-slide images of gastric endoscopic biopsy

HIGHLIGHTS

SUMMARY

Gastric cancer has long been acknowledged as a severe public health problem across the globe with a 5-year survival rate of lower than 40%. Currently, timely and correct diagnosis of gastric cancer relies heavily on pathological examination of gastric biopsy tissue, which is performed by highly trained pathologists with an optical microscopy. Leon et_al proposed two independent approaches based on convolutional neural_network (CNN) for gastric cancer detection using histopathological image samples. More recently, Iizuka et_al investigated the feasibility of CNNs and recurrent neural_networks (RNNs) for classifying WSI into adenocarcinoma, adenoma, and nonneoplastic, achieving . . .

 



If you want to have access to all the content you need to log in!

Thanks :)

Username or E-mail:

If you don't have an account, you can create one here.