1 citations · 1 across the 3 of their papers we have counts for
5 papers
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…
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…
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…