1 citations · 1 across the 6 of their papers we have counts for
6 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…
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…
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…
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…
Localizing Moments in Long Video Via Multimodal Guidance
Wayner Barrios, Mattia Soldan, Alberto Mario Ceballos-Arroyo +2
The recent introduction of the large-scale, long-form MAD and Ego4D datasets has enabled researchers to investigate the performance of current state-of-the-art methods for video gr…