From the 1 of 10 linked papers with an AI index.
10 papers
BATS: Resource-Efficient Volumetric Segmentation with Boundary-Aware Mixed-Resolution Tokens
David Hagerman, Roman Naeem, Fredrik Kahl
BATS is a 3D medical image segmentation model that adaptively uses fine-resolution tokens near predicted class boundaries, reducing memory usage and speeding up inference compared…
CEVAR: Centerline Embedding Extraction for Endovascular Aneurysm Repair
Roman Naeem, Timo Niiniskorpi, Charlotte Sandström +6
Long-term mortality rates after endovascular aneurysm repair (EVAR) remain elevated due to post-EVAR rupture caused by loss of seal in stent graft sealing zones. Structured CT revi…
Trexplorer Super: Topologically Correct Centerline Tree Tracking of Tubular Objects in CT Volumes
Roman Naeem, David Hagerman, Jennifer Alvén +2
Tubular tree structures, such as blood vessels and airways, are essential in human anatomy and accurately tracking them while preserving their topology is crucial for various downs…
SwInception -- Local Attention Meets Convolutions
David Hagerman, Roman Naeem, Jakob Lindqvist +3
Sparse vision transformers have gained popularity as efficient encoders for medical volumetric segmentation, with Swin emerging as a prominent choice. Swin uses local attention to…
ARTA: Adaptive Mixed-Resolution Token Allocation for Efficient Dense Feature Extraction
David Hagerman, Roman Naeem, Erik Brorsson +2
We present ARTA, a mixed-resolution coarse-to-fine vision transformer for efficient dense feature extraction. Unlike models that begin with dense high-resolution (fine) tokens, ART…
RefTr: Recurrent Refinement of Confluent Trajectories for 3D Vascular Tree Centerlines
Roman Naeem, David Hagerman, Jennifer Alvén +1
Tubular tree structures such as blood vessels and lung airways are central to many clinical tasks, including diagnosis, treatment planning, and surgical navigation. Accurate center…