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

Charitable

UNeXt: MLP-Based Rapid Medical Image Segmentation Networkd computational complexity while being able to result in a better representation to help segmentation. The network also consists of skip connections between various levels of encoder and decoder. We test UNeXt on multiple medical image segmentation datasets and show that we reduce the number of para

毗邻

Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentationding high-quality prototypes, in order to make each class distribution compact and separate different classes. We evaluated our SS-Net against five recent methods on the public LA and ACDC datasets. Extensive experimental results under two semi-supervised settings demonstrate the superiority of our

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Pcos971

热情赞扬

Stroke Lesion Segmentation from Low-Quality and Few-Shot MRIs via Similarity-Weighted Self-ensemblinefine the coarse prediction via focusing on the ambiguous regions. To overcome the few-shot challenge, a new Soft Distribution-aware Updating strategy trains the Identify-to-Discern Network in the direction beneficial to tumor segmentation via respective optimizing schemes and adaptive similarity ev

Guaff豪情痛饮

Edge-Oriented Point-Cloud Transformer for 3D Intracranial Aneurysm Segmentationion graph is constructed where connections across the edge are prohibited, thereby dissimilating contexts of points belonging to different categories. Upon that, graph convolution is performed to refine the confusing features via information exchange with dissimilated contexts. In IHE, to further re

Additive

mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentat semantics corresponding to tumor region; a decoder that performs a progressive up-sampling and fusion with the modality-invariant features to generate robust segmentation. Besides, auxiliary regularizers are introduced in both encoder and decoder to further enhance the model’s robustness to incompl

缓解

Multimodal Brain Tumor Segmentation Using Contrastive Learning Based Feature Comparison with Monomodto solve incomparable issue between features learned from multimodal and monomodal images. In the experiments, both in-house and public (BraTS2019) multimodal tumor brain image datasets are used to evaluate our proposed framework, demonstrating better performance compared to the state-of-the-art met

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口诀法

NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentationsted Modality-aware Feature Aggregation (NMaFA) module, which enhances long-term dependencies within individual modalities via a tri-orientated spatial-attention transformer, and further complements key contextual information among modalities via a cross-modality attention transformer. Extensive exp