Multi-modalityReliability learningSegmentationVisual explanationReliability learning and interpretable decision-making are crucial for multi-modality medical image segmentation. Although many works have attempte
2, a lightweight multi-modality medical image semantic segmentation network was conducted. Firstly, the data is augmented through translation, rotation, contrast enhancement, and other expansion methods. Then, the augmentation data is input into the improved UNeXt model, and after training and ...
Multi-modality medical image fusion (MMIF) technology utilizes the complementarity of different modalities to provide more comprehensive diagnostic insights for clinical practice. Existing deep learning-based methods often focus on extracting the primary information from individual modalities while ignoring the...
for incomplete multi-modal medical image segmentation under imbalanced missing rates. Specifically, we first construct pixel-wise and semantic-wise self-distillation to balance the optimization objective of each modality. Then, we define relative preference to evaluate the dominance of each modality during...
为此,作者提出了 Modality-agnostic Domain Generalizable Network (MADGNet),该网络主要包括多频率多尺度注意力块(MFMSA)和集成子解码模块(ESDM),分别用于结合多频率和多尺度特征来改进空间特征提取和减少深度监督下多任务学习中的信息丢失。 网络结构 上图是作者提出的 MADGNet 的总体结构图,整体采用了U-Net的网络...
For the downstream machine-learning task, we focus on the whole-tumor segmentation. From Table 3 and Fig. 3, MM-DSL achieves higher performance than the baselines of FLGAN, FedMed-GAN, and AsynDGAN on multi-modality image generation and whole-tumor segmentation on the BraTS dataset. The ...
A method for automatically displaying an organ of interest includes accessing a series of medical images acquired from a first imaging modality, receiving an input that indicates an organ of interest, automatically detecting the organ of interest within at least one image in the series of medical ...
Analysis https://doi.org/10.1038/s42256-021-00305-2 Code-free deep learning for multi-modality medical image classification Edward Korot 1,2,3, Zeyu Guan 2, Daniel Ferraz1,2,4, Siegfried K. Wagner1, Gongyu Zhang 2, Xiaoxuan Liu 2,5,6, Livia Faes2,7, ...
Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention. [Paper][Code][Project] Diversified and Personalized Multi-rater Medical Image Segmentation. [Paper][Code] MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation ...
1. Firstly, the clinical data is input to the medical image generation framework to generate the target modalities from the given source modalities. Secondly, use the multi-modality MRI data generated in the previous step as the input of the segmentation network to segment the lesions. Thirdly,...