Browsing Department of Computational and Data Sciences (CDS) by Subject "Deep learning"

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Improving photoacoustic imaging with model compensating and deep learning methods

Gutta, Sreedevi

Photoacoustic imaging is a hybrid biomedical imaging technique combining optical ab- sorption contrast with ultrasonic resolution. It is a non-invasive technique that is scalable to reveal structural, functional, and ...
Mitigating Domain Shift via Self-training in Single and Multi-target Unsupervised Domain Adaptation

Kumar, Vikash

Though deep learning has achieved significant successes in many computer vision tasks, the state-of-the-art approaches rely on the availability of a large amount of labeled data for supervision, collection of which is ...
Unsupervised Test-time Adaptation for Patient-Specific Deep Learning Models in Medical Imaging

Ravishankar, Hariharan

Deep learning (DL) models have achieved state-of-the-art results in multiple medical imaging applications, resulting in the widespread adoption of artificial intelligence (AI) models for radiological workflows. Despite ...