1 citations · 1 across the 9 of their papers we have counts for
10 papers
BATS: Resource-Efficient Volumetric Segmentation with Boundary-Aware Mixed-Resolution Tokens
David Hagerman, Roman Naeem, Fredrik Kahl
Many high-performing volumetric segmentation models maintain dense multi-scale feature maps, leading to high activation memory and inference cost. We present BATS (Boundary-Aware T…
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…
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…
Semi-Supervised Hierarchical Open-Set Classification
Erik Wallin, Fredrik Kahl, Lars Hammarstrand
Hierarchical open-set classification handles previously unseen classes by assigning them to the most appropriate high-level category in a class taxonomy. We extend this paradigm to…
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…
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…
ProHOC: Probabilistic Hierarchical Out-of-Distribution Classification via Multi-Depth Networks
Erik Wallin, Fredrik Kahl, Lars Hammarstrand
Out-of-distribution (OOD) detection in deep learning has traditionally been framed as a binary task, where samples are either classified as belonging to the known classes or marked…
Optimizing Gene-Based Testing for Antibiotic Resistance Prediction
David Hagerman, Anna Johnning, Roman Naeem +3
Antibiotic Resistance (AR) is a critical global health challenge that necessitates the development of cost-effective, efficient, and accurate diagnostic tools. Given the genetic ba…
ProSub: Probabilistic Open-Set Semi-Supervised Learning with Subspace-Based Out-of-Distribution Detection
Erik Wallin, Lennart Svensson, Fredrik Kahl +1
In open-set semi-supervised learning (OSSL), we consider unlabeled datasets that may contain unknown classes. Existing OSSL methods often use the softmax confidence for classifying…