Zobrazeno 1 - 10
of 1 118
pro vyhledávání: '"Chen, Wenting"'
Gaze estimation is pivotal in human scene comprehension tasks, particularly in medical diagnostic analysis. Eye-tracking technology facilitates the recording of physicians' ocular movements during image interpretation, thereby elucidating their visua
Externí odkaz:
http://arxiv.org/abs/2408.05502
Autor:
Li, Cheng-Yi, Chang, Kao-Jung, Yang, Cheng-Fu, Wu, Hsin-Yu, Chen, Wenting, Bansal, Hritik, Chen, Ling, Yang, Yi-Ping, Chen, Yu-Chun, Chen, Shih-Pin, Lirng, Jiing-Feng, Chang, Kai-Wei, Chiou, Shih-Hwa
Multi-modal large language models (MLLMs) have been given free rein to explore exciting medical applications with a primary focus on radiology report generation. Nevertheless, the preliminary success in 2D radiology captioning is incompetent to refle
Externí odkaz:
http://arxiv.org/abs/2407.02235
Pathology image are essential for accurately interpreting lesion cells in cytopathology screening, but acquiring high-resolution digital slides requires specialized equipment and long scanning times. Though super-resolution (SR) techniques can allevi
Externí odkaz:
http://arxiv.org/abs/2406.18310
Autor:
Ruffini, Filippo, Tronchin, Lorenzo, Wu, Zhuoru, Chen, Wenting, Soda, Paolo, Shen, Linlin, Guarrasi, Valerio
In the fight against the COVID-19 pandemic, leveraging artificial intelligence to predict disease outcomes from chest radiographic images represents a significant scientific aim. The challenge, however, lies in the scarcity of large, labeled datasets
Externí odkaz:
http://arxiv.org/abs/2405.13771
Autor:
Li, Hongwei Bran, Navarro, Fernando, Ezhov, Ivan, Bayat, Amirhossein, Das, Dhritiman, Kofler, Florian, Shit, Suprosanna, Waldmannstetter, Diana, Paetzold, Johannes C., Hu, Xiaobin, Wiestler, Benedikt, Zimmer, Lucas, Amiranashvili, Tamaz, Prabhakar, Chinmay, Berger, Christoph, Weidner, Jonas, Alonso-Basant, Michelle, Rashid, Arif, Baid, Ujjwal, Adel, Wesam, Ali, Deniz, Baheti, Bhakti, Bai, Yingbin, Bhatt, Ishaan, Cetindag, Sabri Can, Chen, Wenting, Cheng, Li, Dutand, Prasad, Dular, Lara, Elattar, Mustafa A., Feng, Ming, Gao, Shengbo, Huisman, Henkjan, Hu, Weifeng, Innani, Shubham, Jiat, Wei, Karimi, Davood, Kuijf, Hugo J., Kwak, Jin Tae, Le, Hoang Long, Lia, Xiang, Lin, Huiyan, Liu, Tongliang, Ma, Jun, Ma, Kai, Ma, Ting, Oksuz, Ilkay, Holland, Robbie, Oliveira, Arlindo L., Pal, Jimut Bahan, Pei, Xuan, Qiao, Maoying, Saha, Anindo, Selvan, Raghavendra, Shen, Linlin, Silva, Joao Lourenco, Spiclin, Ziga, Talbar, Sanjay, Wang, Dadong, Wang, Wei, Wang, Xiong, Wang, Yin, Xia, Ruiling, Xu, Kele, Yan, Yanwu, Yergin, Mert, Yu, Shuang, Zeng, Lingxi, Zhang, YingLin, Zhao, Jiachen, Zheng, Yefeng, Zukovec, Martin, Do, Richard, Becker, Anton, Simpson, Amber, Konukoglu, Ender, Jakab, Andras, Bakas, Spyridon, Joskowicz, Leo, Menze, Bjoern
Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentat
Externí odkaz:
http://arxiv.org/abs/2405.18435
Autor:
Ma, Chong, Jiang, Hanqi, Chen, Wenting, Li, Yiwei, Wu, Zihao, Yu, Xiaowei, Liu, Zhengliang, Guo, Lei, Zhu, Dajiang, Zhang, Tuo, Shen, Dinggang, Liu, Tianming, Li, Xiang
In the medical multi-modal frameworks, the alignment of cross-modality features presents a significant challenge. However, existing works have learned features that are implicitly aligned from the data, without considering the explicit relationships
Externí odkaz:
http://arxiv.org/abs/2403.12416
Data scarcity and privacy concerns limit the availability of high-quality medical images for public use, which can be mitigated through medical image synthesis. However, current medical image synthesis methods often struggle to accurately capture the
Externí odkaz:
http://arxiv.org/abs/2403.06835
Autor:
Wang, Wenxuan, Su, Yihang, Huan, Jingyuan, Liu, Jie, Chen, Wenting, Zhang, Yudi, Li, Cheng-Yi, Chang, Kao-Jung, Xin, Xiaohan, Shen, Linlin, Lyu, Michael R.
The significant breakthroughs of Medical Multi-Modal Large Language Models (Med-MLLMs) renovate modern healthcare with robust information synthesis and medical decision support. However, these models are often evaluated on benchmarks that are unsuita
Externí odkaz:
http://arxiv.org/abs/2402.11217
To address these issues, we propose a novel Adaptive patch-word Matching (AdaMatch) model to correlate chest X-ray (CXR) image regions with words in medical reports and apply it to CXR-report generation to provide explainability for the generation pr
Externí odkaz:
http://arxiv.org/abs/2312.08078
Autor:
Yang, Xinquan, Li, Xuguang, Li, Xuechen, Chen, Wenting, Shen, Linlin, Li, Xin, Deng, Yongqiang
Publikováno v:
Expert Systems With Applications 2023
In implant prosthesis treatment, the design of the surgical guide heavily relies on the manual location of the implant position, which is subjective and prone to doctor's experiences. When deep learning based methods has started to be applied to addr
Externí odkaz:
http://arxiv.org/abs/2305.10044