Zobrazeno 1 - 10
of 124
pro vyhledávání: '"Tian, Chunna"'
Semi-supervised segmentation presents a promising approach for large-scale medical image analysis, effectively reducing annotation burdens while achieving comparable performance. This methodology holds substantial potential for streamlining the segme
Externí odkaz:
http://arxiv.org/abs/2404.07032
Source-free test-time adaptation for medical image segmentation aims to enhance the adaptability of segmentation models to diverse and previously unseen test sets of the target domain, which contributes to the generalizability and robustness of medic
Externí odkaz:
http://arxiv.org/abs/2310.11766
Semi-supervised medical image segmentation offers a promising solution for large-scale medical image analysis by significantly reducing the annotation burden while achieving comparable performance. Employing this method exhibits a high degree of pote
Externí odkaz:
http://arxiv.org/abs/2305.16216
Consistency learning plays a crucial role in semi-supervised medical image segmentation as it enables the effective utilization of limited annotated data while leveraging the abundance of unannotated data. The effectiveness and efficiency of consiste
Externí odkaz:
http://arxiv.org/abs/2305.16214
The Stable Diffusion model is a prominent text-to-image generation model that relies on a text prompt as its input, which is encoded using the Contrastive Language-Image Pre-Training (CLIP). However, text prompts have limitations when it comes to inc
Externí odkaz:
http://arxiv.org/abs/2305.12716
Autor:
Zhou, Heng, Tian, Chunna, Zhang, Zhenxi, Li, Chengyang, Ding, Yuxuan, Xie, Yongqiang, Li, Zhongbo
RGB-Thermal salient object detection (SOD) combines two spectra to segment visually conspicuous regions in images. Most existing methods use boundary maps to learn the sharp boundary. These methods ignore the interactions between isolated boundary pi
Externí odkaz:
http://arxiv.org/abs/2209.10158
The Contrastive Language-Image Pre-training (CLIP) Model is a recently proposed large-scale pre-train model which attracts increasing attention in the computer vision community. Benefiting from its gigantic image-text training set, the CLIP model has
Externí odkaz:
http://arxiv.org/abs/2207.09248
Semi-supervised learning methods have been explored in medical image segmentation tasks due to the scarcity of pixel-level annotation in the real scenario. Proto-type alignment based consistency constraint is an intuitional and plausible solu-tion to
Externí odkaz:
http://arxiv.org/abs/2206.01739
A key challenge of infrared small target segmentation (ISTS) is to balance false negative pixels (FNs) and false positive pixels (FPs). Traditional methods combine FNs and FPs into a single objective by weighted sum, and the optimization process is d
Externí odkaz:
http://arxiv.org/abs/2205.13124
Publikováno v:
In Neurocomputing 1 October 2024 600