LOPE
发表于 2025-3-23 13:40:24
ted in turn, and the data is transferred between different modules to realize the knowledge transfer and collaborative training. The final prediction is obtained by a voting result of two classifiers. Experimental results on three pneumonia databases demonstrate the effectiveness of our framework wi
小鹿
发表于 2025-3-23 15:08:36
Transformer captures the relevance between multi-phase CEMRI for multi-phase CEMRI information fusion and selection. Lastly, we proposed a multi-level training strategy, which enables an enhanced loss function to improve the quantification task. The mfTrans-Net is validated on multi-phase CEMRI of
外观
发表于 2025-3-23 21:33:35
Neil A. James,Anika Großeonal network (GQ-GCN), which adopts CNN to extract features from histopathological images for further adaptively graph construction. In particular, the group graph convolutional network (G-GCN) is developed to implement both feature selection and compression of graph representation. In addition, the
相容
发表于 2025-3-23 23:41:38
Arron Wilde Tippetts with a quadtree strategy for acceleration. Experimental results on two WSI datasets highlight two merits of our framework: 1) effectively aggregate multi-resolution information to achieve better results, 2) significantly reduce the computational cost to accelerate the prediction without decreasing
拥挤前
发表于 2025-3-24 04:42:22
Neil A. Jamesentation performance, resulting in more accurate FAZ contours and fewer outliers. Moreover, both low-level and high-level features from the aforementioned three branches, including shape, size, boundary, and signed directional distance map of FAZ, are fused hierarchically with features from the diag
chiropractor
发表于 2025-3-24 08:15:01
aset can be transformed from . to . via progressive pixel-wise soft-correction, thus providing productive guidance. Extensive experiments using two public datasets demonstrate the superiority of the proposed framework as well as the effectiveness of each component.
勉励
发表于 2025-3-24 14:29:23
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Hallmark
发表于 2025-3-24 18:01:00
Martin Charterepresentations. In the second stage, the decentralized partially labeled data are exploited to learn an energy-based multi-label classifier for the common classes. Finally, the underrepresented classes are detected based on the energy and a .-based nearest-neighbor model is proposed for few-shot mat
Metastasis
发表于 2025-3-24 19:34:45
Hajnalka Vaagen,Arron Wilde Tippettepresentations. In the second stage, the decentralized partially labeled data are exploited to learn an energy-based multi-label classifier for the common classes. Finally, the underrepresented classes are detected based on the energy and a .-based nearest-neighbor model is proposed for few-shot mat
interrupt
发表于 2025-3-24 23:28:53
Arron Wilde Tippettions, is able to match the heat distribution computed by a finite-difference solver with a root mean squared error of 0.51 ± 0.50 .C and the estimated ablation zone with a mean dice score of 0.93 ± 0.05, while being over 100 times faster. When applied to single electrode automatic ablation planning