2 citations · 3 across the 5 of their papers we have counts for
4 papers · 1 filter
Quality-Guided Semi-Supervised Learning for Medical Image Segmentation
Kumar Abhishek, Ghassan Hamarneh
Training accurate medical image segmentation models requires large amounts of densely annotated data, which is costly and time-consuming to obtain. Semi-supervised learning (SSL) a…
IMA++: ISIC Archive Multi-Annotator Dermoscopic Skin Lesion Segmentation Dataset
Kumar Abhishek, Jeremy Kawahara, Ghassan Hamarneh
Multi-annotator medical image segmentation is an important research problem, but requires annotated datasets that are expensive to collect. Dermoscopic skin lesion imaging allows h…
Debiasify: Self-Distillation for Unsupervised Bias Mitigation
Nourhan Bayasi, Jamil Fayyad, Ghassan Hamarneh +2
Simplicity bias poses a significant challenge in neural networks, often leading models to favor simpler solutions and inadvertently learn decision rules influenced by spurious corr…
: Representing Anatomical Trees by Denoising Diffusion of Implicit Neural Fields
Ashish Sinha, Ghassan Hamarneh
Anatomical trees play a central role in clinical diagnosis and treatment planning. However, accurately representing anatomical trees is challenging due to their varying and complex…