8 citations · 24 across the 14 of their papers we have counts for
4 papers · 1 filter
Fréchet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets
Nicholas Konz, Richard Osuala, Preeti Verma +16
Determining whether two sets of images belong to the same or different distributions or domains is a crucial task in modern medical image analysis and deep learning; for example, t…
Segment anything model 2: an application to 2D and 3D medical images
Haoyu Dong, Hanxue Gu, Yaqian Chen +3
Segment Anything Model (SAM) has gained significant attention because of its ability to segment various objects in images given a prompt. The recently developed SAM 2 has extended…
ContourDiff: Unpaired Medical Image Translation with Structural Consistency
Yuwen Chen, Nicholas Konz, Hanxue Gu +5
Accurately translating medical images between different modalities, such as Computed Tomography (CT) to Magnetic Resonance Imaging (MRI), has numerous downstream clinical and machi…
SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI
Hanxue Gu, Roy Colglazier, Haoyu Dong +20
Magnetic Resonance Imaging (MRI) is pivotal in radiology, offering non-invasive and high-quality insights into the human body. Precise segmentation of MRIs into different organs an…