Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022; 25th International C Linwei Wang,Qi Dou,Shuo Li Conference procee

協(xié)迫

一大塊

instill

Campaign

老人病學(xué)

Uni4Eye: Unified 2D and?3D Self-supervised Pre-training via?Masked Image Modeling Transformer for?Opision Transformer (ViT) architecture. We employ a Unified Patch Embedding module to replace the origin patch embedding module in ViT for jointly processing both 2D and 3D input images. Besides, we design a dual-branch multitask decoder module to simultaneously perform two reconstruction tasks on the

ORE

Cloudburst

Calibrating Label Distribution for?Class-Imbalanced Barely-Supervised Knee Segmentationre weighted form and 2) label position distribution for constructing a cropping probability mask to crop more sub-volumes in cartilage areas from both labeled and unlabeled inputs. In addition, we design dual uncertainty-aware sampling supervision to enhance the supervision of low-confident categori

傀儡

Semi-supervised Medical Image Classification with?Temporal Knowledge-Aware Regularizationoss of unlabeled samples according to the temporal information across training iterations, to adaptively relax pseudo labels. To release the excessive dependency of biased pseudo labels, we take advantage of the temporal knowledge and propose Iterative Prototype Harmonizing (IPH) to encourage the mo

虛構(gòu)的東西

直覺沒有