独行者
发表于 2025-3-23 09:46:11
Sebastian A. Gerlachppearance consistency of the same polyp, an image consistency (IC) loss is designed. Such IC loss explicitly narrows the distance between features extracted by two different networks, which improves the robustness of the model. Note that our BoxPolyp is a plug-and-play model, which can be merged int
LIMIT
发表于 2025-3-23 16:10:26
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hegemony
发表于 2025-3-23 21:27:56
Sebastian A. Gerlachrove the overall performance of the model compared to state-of-the-art segmentation methods. We explore four different datasets and provide insights for size-specific improvements. Our proposed . (TGANet) can generalize well to variable-sized polyps in different datasets. Codes are available at ..
B-cell
发表于 2025-3-24 00:26:45
Sebastian A. Gerlach multi-scale framework to predict the displacement fields in a coarse-to-fine manner, superior performance can be achieved compared with traditional and learning-based approaches. Comprehensive experiments on two public datasets are conducted to demonstrate the effectiveness of the proposed Deformer
Hemiplegia
发表于 2025-3-24 04:19:11
Sebastian A. Gerlachtasets, we identified and confirmed a small number of landmarks with potential localization errors and found that, in some cases, the landmark distribution was not ideal for an unbiased assessment of non-rigid registration errors. Under discussion, we provide some constructive suggestions for improv
争论
发表于 2025-3-24 09:21:51
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Melatonin
发表于 2025-3-24 12:25:25
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Mendicant
发表于 2025-3-24 17:29:50
Sebastian A. Gerlachto accommodate newly emerging classes. These heads enable independent predictions for newly introduced and previously learned classes, effectively minimizing the impact of new classes on old ones during the course of continual learning. We further propose incorporating Contrastive Language-Image Pre
STENT
发表于 2025-3-24 21:25:25
Sebastian A. Gerlachuests or inflated computation costs. This paper introduces a novel technique for selecting annotation regions adaptively, mitigating the reliance on this AL hyperparameter. Specifically, we dynamically determine each region by first identifying an informative area and then detecting its optimal boun
真繁荣
发表于 2025-3-25 01:31:38
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