Zobrazeno 1 - 10
of 453
pro vyhledávání: '"Dalca A"'
Medical researchers and clinicians often need to perform novel segmentation tasks on a set of related images. Existing methods for segmenting a new dataset are either interactive, requiring substantial human effort for each image, or require an exist
Externí odkaz:
http://arxiv.org/abs/2412.15058
Autor:
Dey, Neel, Billot, Benjamin, Wong, Hallee E., Wang, Clinton J., Ren, Mengwei, Grant, P. Ellen, Dalca, Adrian V., Golland, Polina
Current volumetric biomedical foundation models struggle to generalize as public 3D datasets are small and do not cover the broad diversity of medical procedures, conditions, anatomical regions, and imaging protocols. We address this by creating a re
Externí odkaz:
http://arxiv.org/abs/2411.02372
We present VoxelPrompt, an agent-driven vision-language framework that tackles diverse radiological tasks through joint modeling of natural language, image volumes, and analytical metrics. VoxelPrompt is multi-modal and versatile, leveraging the flex
Externí odkaz:
http://arxiv.org/abs/2410.08397
We present a keypoint-based foundation model for general purpose brain MRI registration, based on the recently-proposed KeyMorph framework. Our model, called BrainMorph, serves as a tool that supports multi-modal, pairwise, and scalable groupwise reg
Externí odkaz:
http://arxiv.org/abs/2405.14019
Autor:
Kelley, William, Ngo, Nathan, Dalca, Adrian V., Fischl, Bruce, Zöllei, Lilla, Hoffmann, Malte
Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts t
Externí odkaz:
http://arxiv.org/abs/2402.16634
Autor:
Rakic, Marianne, Wong, Hallee E., Ortiz, Jose Javier Gonzalez, Cimini, Beth, Guttag, John, Dalca, Adrian V.
Existing learning-based solutions to medical image segmentation have two important shortcomings. First, for most new segmentation task, a new model has to be trained or fine-tuned. This requires extensive resources and machine learning expertise, and
Externí odkaz:
http://arxiv.org/abs/2401.13650
Biomedical image segmentation is a crucial part of both scientific research and clinical care. With enough labelled data, deep learning models can be trained to accurately automate specific biomedical image segmentation tasks. However, manually segme
Externí odkaz:
http://arxiv.org/abs/2312.07381
Autor:
Li, Jian, Tuckute, Greta, Fedorenko, Evelina, Edlow, Brian L., Dalca, Adrian V., Fischl, Bruce
Surface-based cortical registration is an important topic in medical image analysis and facilitates many downstream applications. Current approaches for cortical registration are mainly driven by geometric features, such as sulcal depth and curvature
Externí odkaz:
http://arxiv.org/abs/2311.08544
Autor:
Su, Ruisheng, van der Sluijs, Matthijs, Cornelissen, Sandra, van Zwam, Wim, van der Lugt, Aad, Niessen, Wiro, Ruijters, Danny, van Walsum, Theo, Dalca, Adrian
Cerebral X-ray digital subtraction angiography (DSA) is the standard imaging technique for visualizing blood flow and guiding endovascular treatments. The quality of DSA is often negatively impacted by body motion during acquisition, leading to decre
Externí odkaz:
http://arxiv.org/abs/2310.05445
Recent vision-language models outperform vision-only models on many image classification tasks. However, because of the absence of paired text/image descriptions, it remains difficult to fine-tune these models for fine-grained image classification. I
Externí odkaz:
http://arxiv.org/abs/2307.11315