5 papers
Scaling up fine-grained intracranial vessel annotations in computed tomography angiography
Chu-Hsuan Lin, Alberto Mario Ceballos-Arroyo, Jisoo Kim +4
In this work, we present SemanticVessel, a dataset for fine-grained brain vessel segmentation in computed tomography angiography scans. Based on the detailed contrast provided by d…
Do Activation Verbalization Methods Convey Privileged Information?
Millicent Li, Alberto Mario Ceballos Arroyo, Giordano Rogers +2
Recent interpretability methods have proposed to translate LLM internal representations into natural language descriptions using a second verbalizer LLM. This is intended to illumi…
Robust automatic brain vessel segmentation in 3D CTA scans using dynamic 4D-CTA data
Alberto Mario Ceballos-Arroyo, Shrikanth M. Yadav, Chu-Hsuan Lin +4
In this study, we develop a novel methodology for annotating the brain vasculature using dynamic 4D-CTA head scans. By using multiple time points from dynamic CTA acquisitions, we…
Automated anatomy-based post-processing reduces false positives and improved interpretability of deep learning intracranial aneurysm detection
Jisoo Kim, Chu-Hsuan Lin, Alberto Ceballos-Arroyo +6
Introduction: Deep learning (DL) models can help detect intracranial aneurysms on CTA, but high false positive (FP) rates remain a barrier to clinical translation, despite improvem…
Anatomically-guided masked autoencoder pre-training for aneurysm detection
Alberto Mario Ceballos-Arroyo, Jisoo Kim, Chu-Hsuan Lin +3
Intracranial aneurysms are a major cause of morbidity and mortality worldwide, and detecting them manually is a complex, time-consuming task. Albeit automated solutions are desirab…