Zobrazeno 1 - 10
of 355
pro vyhledávání: '"Huisman, HenkJan"'
Autor:
Li, Hao, Liu, Han, von Busch, Heinrich, Grimm, Robert, Huisman, Henkjan, Tong, Angela, Winkel, David, Penzkofer, Tobias, Shabunin, Ivan, Choi, Moon Hyung, Yang, Qingsong, Szolar, Dieter, Shea, Steven, Coakley, Fergus, Harisinghani, Mukesh, Oguz, Ipek, Comaniciu, Dorin, Kamen, Ali, Lou, Bin
Publikováno v:
Radiology: Artificial Intelligence 2024;6(5):e230521
Our hypothesis is that UDA using diffusion-weighted images, generated with a unified model, offers a promising and reliable strategy for enhancing the performance of supervised learning models in multi-site prostate lesion detection, especially when
Externí odkaz:
http://arxiv.org/abs/2408.04777
Autor:
Hering, Alessa, de Boer, Sarah, Saha, Anindo, Twilt, Jasper J., Heinrich, Mattias P., Yakar, Derya, de Rooij, Maarten, Huisman, Henkjan, Bosma, Joeran S.
The PI-CAI (Prostate Imaging: Cancer AI) challenge led to expert-level diagnostic algorithms for clinically significant prostate cancer detection. The algorithms receive biparametric MRI scans as input, which consist of T2-weighted and diffusion-weig
Externí odkaz:
http://arxiv.org/abs/2404.09666
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
Quality of deep convolutional neural network predictions strongly depends on the size of the training dataset and the quality of the annotations. Creating annotations, especially for 3D medical image segmentation, is time-consuming and requires exper
Externí odkaz:
http://arxiv.org/abs/2305.05984
Diffusion models for text-to-image generation, known for their efficiency, accessibility, and quality, have gained popularity. While inference with these systems on consumer-grade GPUs is increasingly feasible, training from scratch requires large ca
Externí odkaz:
http://arxiv.org/abs/2303.13430
Autor:
Li, Yiwen, Fu, Yunguan, Gayo, Iani, Yang, Qianye, Min, Zhe, Saeed, Shaheer, Yan, Wen, Wang, Yipei, Noble, J. Alison, Emberton, Mark, Clarkson, Matthew J., Huisman, Henkjan, Barratt, Dean, Prisacariu, Victor Adrian, Hu, Yipeng
The prowess that makes few-shot learning desirable in medical image analysis is the efficient use of the support image data, which are labelled to classify or segment new classes, a task that otherwise requires substantially more training images and
Externí odkaz:
http://arxiv.org/abs/2209.05160
Autor:
Li, Yiwen, Fu, Yunguan, Yang, Qianye, Min, Zhe, Yan, Wen, Huisman, Henkjan, Barratt, Dean, Prisacariu, Victor Adrian, Hu, Yipeng
The ability to adapt medical image segmentation networks for a novel class such as an unseen anatomical or pathological structure, when only a few labelled examples of this class are available from local healthcare providers, is sought-after. This po
Externí odkaz:
http://arxiv.org/abs/2201.06358
Autor:
Bosma, Joeran S., Saha, Anindo, Hosseinzadeh, Matin, Slootweg, Ilse, de Rooij, Maarten, Huisman, Henkjan
Publikováno v:
Radiology: Artificial Intelligence, 2023:e230031
Deep learning-based diagnostic performance increases with more annotated data, but large-scale manual annotations are expensive and labour-intensive. Experts evaluate diagnostic images during clinical routine, and write their findings in reports. Lev
Externí odkaz:
http://arxiv.org/abs/2112.05151
Autor:
Alves, Natália, Schuurmans, Megan, Litjens, Geke, Bosma, Joeran S., Hermans, John, Huisman, Henkjan
Publikováno v:
lves, N.; Schuurmans, M.;Litjens, G.; Bosma, J.S.; Hermans, J.;Huisman, H. Fully Automatic DeepLearning Framework for PancreaticDuctal Adenocarcinoma Detection onComputed Tomography.Cancers2022,14, 376
Early detection improves prognosis in pancreatic ductal adenocarcinoma (PDAC) but is challenging as lesions are often small and poorly defined on contrast-enhanced computed tomography scans (CE-CT). Deep learning can facilitate PDAC diagnosis, howeve
Externí odkaz:
http://arxiv.org/abs/2111.15409
We hypothesize that probabilistic voxel-level classification of anatomy and malignancy in prostate MRI, although typically posed as near-identical segmentation tasks via U-Nets, require different loss functions for optimal performance due to inherent
Externí odkaz:
http://arxiv.org/abs/2110.12889