MORT
发表于 2025-3-23 11:23:34
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非秘密
发表于 2025-3-23 16:15:15
Guidewire Segmentation in 4D Ultrasound Sequences Using Recurrent Fully Convolutional Networks compared to standard single-frame model predictions in a way that is not simply associated to an increase in model size. Additionally, we demonstrate that our approach may be combined with traditional techniques such as active splines to further enhance stability over time.
轻信
发表于 2025-3-23 21:40:20
Embedding Weighted Feature Aggregation Network with Domain Knowledge Integration for Breast Ultrasouategory information is applied as the classification label. (II) In order to deal with the artifacts in ultrasound, such as posterior shadowing, we conduct Squeeze-and-Excitation (SE) block and aggregation mechanism to compose the up-sampling part in U-Net. (III) We employ the conditional random fie
肿块
发表于 2025-3-24 00:46:58
A Curriculum Learning Based Approach to Captioning Ultrasound Imagesasserstein distance for image information and tf-idf metric for text information. The evaluation results show an improvement in all performance metrics when using curriculum learning over stochastic mini-batch training for the individual task of image classification as well as using a dual curriculu
buoyant
发表于 2025-3-24 05:33:22
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inundate
发表于 2025-3-24 09:12:01
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细颈瓶
发表于 2025-3-24 14:01:50
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离开可分裂
发表于 2025-3-24 15:02:16
Label Efficient Localization of Fetal Brain Biometry Planes in Ultrasound Through Metric Learningork is semi-supervised consisting of two major components: 1) a prototypical learning module that learns categorical embeddings implicitly to prevent the model from overfitting; and, 2) a semantic transfer module (to unlabelled data) that performs “temperature modulated” entropy minimization to enco
Complement
发表于 2025-3-24 21:15:22
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小口啜饮
发表于 2025-3-25 00:38:06
Unsupervised Cross-domain Image Classification by Distance Metric Guided Feature Alignmentrates class distribution alignment to transfer semantic knowledge from a source domain to a target domain. We evaluate the proposed method on fetal ultrasound datasets for cross-device image classification. Experimental results demonstrate that the proposed method outperforms the state-of-the-art an