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

蹣跚

irradicable

Multimodal Brain Tumor Segmentation Using Contrastive Learning Based Feature Comparison with?Monomodl structure to enhance the capacity of learning tumor-related features, while other valuable related information, such as normal brain appearance, is often ignored. Inspired by the fact that radiologists are often trained to compare with normal tissues when identifying tumor regions, in this paper,

Liberate

Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation techniques have recently achieved promising cross-modality medical image segmentation by transferring knowledge from a label-rich source domain to an unlabeled target domain. However, it is also difficult to collect annotations from the source domain in many clinical applications, rendering most pr

MEAN

NestedFormer: Nested Modality-Aware Transformer for?Brain Tumor Segmentationevious multi-modal MRI segmentation methods usually perform modal fusion by concatenating multi-modal MRIs at an early/middle stage of the network, which hardly explores non-linear dependencies between modalities. In this work, we propose a novel Nested Modality-Aware Transformer (NestedFormer) to e

溫室

MaxStyle: Adversarial Style Composition for?Robust Medical Image Segmentationme domain, yet their performance can degrade significantly on unseen domains, which hinders the deployment of CNNs in many clinical scenarios. Most existing works improve model out-of-domain (OOD) robustness by collecting multi-domain datasets for training, which is expensive and may not always be f

ciliary-body

A Robust Volumetric Transformer for?Accurate 3D Tumor Segmentationobal spatial cues, and preserving information along all axes of the volume. Encoder of the proposed design benefits from self-attention mechanism to simultaneously encode local and global cues, while the decoder employs a parallel self and cross attention formulation to capture fine details for boun

Indolent

Usable Region Estimate for?Assessing Practical Usability of?Medical Image Segmentation Models’s predictions can be used/trusted. We first propose a measure, Correctness-Confidence Rank Correlation (CCRC), to capture how predictions’ confidence estimates correlate with their correctness scores in rank. A model with a high value of CCRC means its prediction confidences reliably suggest which

Harrowing

指派

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星球的光亮度

lly uncover new innovative ideas.Practical preparation of th.The textbook contains a comprehensive presentation of tools that can be used to systematically generate innovative ideas for new business growth ("opportunities"). In practice, it can be observed that companies make considerable efforts to