基本信息
浏览量:44
职业迁徙
个人简介
A deep learning research scientist currently working on interpretable deep learning using deep generative models for 3D medical imaging datasets.
Machine/ Deep learning techniques:
Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Capsule Networks, Convolutional Neural Networks (CNNs), Long-Short Term Memory (LSTM) Networks, Support Vector Machines (SVMs), Principal Component Analysis (PCA), interest point detectors, feature descriptors, random forests, regression, feature attribution, image-to-image translation, image synthesis, segmentation, classification, regression, image augmentation, raw data labelling and preparation.
Machine/ Deep learning techniques:
Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Capsule Networks, Convolutional Neural Networks (CNNs), Long-Short Term Memory (LSTM) Networks, Support Vector Machines (SVMs), Principal Component Analysis (PCA), interest point detectors, feature descriptors, random forests, regression, feature attribution, image-to-image translation, image synthesis, segmentation, classification, regression, image augmentation, raw data labelling and preparation.
研究兴趣
论文共 18 篇作者统计合作学者相似作者
按年份排序按引用量排序主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
Elizabeth Walsh,Salim Arslan,Andre Geraldes,Andrew Hanby,Rebecca Millican-Slater, Nicholas Bennett, Bejal Mistry,Julian Schmidt, Steffen Wolf,Cher Bass, Foivos Ntelemis, Naren Kumar,
Journal of Clinical Oncologyno. 16_suppl (2024): 562-562
Communications Medicineno. 1 (2024): 1-15
S. Arslan, P. Pandya, F. Ntelemis, S. Wolf, J. Schmidt, A. Geraldes, A. Bazaga, D. Mehrotra, J. Nyonyintono, S. Singhal,C. Bass, O.M. Carlos,
Annals of Oncology (2023): S718-S718
Salim Arslan,Xiusi Li,Julian Schmidt,Julius Hense,Andre Geraldes,Cher Bass, Keelan Brown, Angelica Marcia, Tim Dewhirst,Pahini Pandya,Shikha Singhal,Debapriya Mehrotra,
biorxiv(2022)
bioRxiv (Cold Spring Harbor Laboratory) (2022)
bioRxiv (2021)
NIPS 2020 (2020): 7697-7709
引用40浏览0EI引用
40
0
加载更多
作者统计
合作学者
合作机构
D-Core
- 合作者
- 学生
- 导师
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn