13 citations · 20 across the 6 of their papers we have counts for
3 papers · 1 filter
Knowledge-based anomaly detection for identifying network-induced shape artifacts
Rucha Deshpande, Tahsin Rahman, Miguel Lago +6
Synthetic data provides a promising approach to address data scarcity for training machine learning models; however, adoption without proper quality assessments may introduce artif…
T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Images
Christopher Wiedeman, Anastasiia Sarmakeeva, Elena Sizikova +4
One of the key impediments for developing and assessing robust medical imaging algorithms is limited access to large-scale datasets with suitable annotations. Synthetic data genera…
S-SYNTH: Knowledge-Based, Synthetic Generation of Skin Images
Andrea Kim, Niloufar Saharkhiz, Elena Sizikova +4
Development of artificial intelligence (AI) techniques in medical imaging requires access to large-scale and diverse datasets for training and evaluation. In dermatology, obtaining…