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Humans can generalize to any given new task with few examples, while current deep learning algorithms do not generalize with few labels and require a large number of annotations to yield high performance. This problem is more relevant for deep learning algorithms applied to medical image analysis tasks. For medical imaging, obtaining large datasets and corresponding annotations for each new task from clinical experts is expensive and time-consuming, and not a preferable solution in clinical settings.
The focus of my PhD was to solve this problem and achieve high performance with deep learning algorithms using few annotations while leveraging unlabeled data for medical imaging tasks. My research topics broadly include: (a) Self-supervised learning, (b) Data augmentation, (c) Semi-supervised learning.
The focus of my PhD was to solve this problem and achieve high performance with deep learning algorithms using few annotations while leveraging unlabeled data for medical imaging tasks. My research topics broadly include: (a) Self-supervised learning, (b) Data augmentation, (c) Semi-supervised learning.
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论文共 21 篇作者统计合作学者相似作者
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MIDLpp.219-230, (2022)
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Florian A Huber,Krishna Chaitanya, Nico Gross,Sunand Reddy Chinnareddy, Felix Gross,Ender Konukoglu,Roman Guggenberger
arxiv(2022)
Florian A. Huber,Krishna Chaitanya, Nico Gross,Sunand Reddy Chinnareddy, Felix Gross,Ender Konukoglu,Roman Guggenberger
Investigative Radiologyno. 1 (2021): 33-43
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