Zobrazeno 1 - 10
of 236
pro vyhledávání: '"Kim, Heejong"'
Autor:
Kim, Heejong, Milecki, Leo, Moghadam, Mina C, Liu, Fengbei, Nguyen, Minh, Qiu, Eric, Thanki, Abhishek, Sabuncu, Mert R
Segmentation is a crucial task in the medical imaging field and is often an important primary step or even a prerequisite to the analysis of medical volumes. Yet treatments such as surgery complicate the accurate delineation of regions of interest. T
Externí odkaz:
http://arxiv.org/abs/2409.08143
Distribution shifts between sites can seriously degrade model performance since models are prone to exploiting unstable correlations. Thus, many methods try to find features that are stable across sites and discard unstable features. However, unstabl
Externí odkaz:
http://arxiv.org/abs/2409.05996
Autor:
Nguyen, Minh, Karaman, Batuhan K., Kim, Heejong, Wang, Alan Q., Liu, Fengbei, Sabuncu, Mert R.
Deep learning models can extract predictive and actionable information from complex inputs. The richer the inputs, the better these models usually perform. However, models that leverage rich inputs (e.g., multi-modality) can be difficult to deploy wi
Externí odkaz:
http://arxiv.org/abs/2405.20448
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
Healthcare data often come from multiple sites in which the correlations between confounding variables can vary widely. If deep learning models exploit these unstable correlations, they might fail catastrophically in unseen sites. Although many metho
Externí odkaz:
http://arxiv.org/abs/2310.15766
Autor:
Wang, Alan Q., Karaman, Batuhan K., Kim, Heejong, Rosenthal, Jacob, Saluja, Rachit, Young, Sean I., Sabuncu, Mert R.
Interpretability for machine learning models in medical imaging (MLMI) is an important direction of research. However, there is a general sense of murkiness in what interpretability means. Why does the need for interpretability in MLMI arise? What go
Externí odkaz:
http://arxiv.org/abs/2310.01685
Most state-of-the-art techniques for medical image segmentation rely on deep-learning models. These models, however, are often trained on narrowly-defined tasks in a supervised fashion, which requires expensive labeled datasets. Recent advances in se
Externí odkaz:
http://arxiv.org/abs/2307.03266
Autor:
Kim, Heejong, Sabuncu, Mert R.
Longitudinal studies, where a series of images from the same set of individuals are acquired at different time-points, represent a popular technique for studying and characterizing temporal dynamics in biomedical applications. The classical approach
Externí odkaz:
http://arxiv.org/abs/2304.02531
Autor:
Kim, Heejong, Hua, Yuexuan, Chen, Chin-Tu, Epel, Boris, Sundramoorthy, Subramanian, Halpern, Howard, Kao, Chien-Min
Publikováno v:
In Nuclear Inst. and Methods in Physics Research, A June 2024 1063
Current deep learning approaches for diffusion MRI modeling circumvent the need for densely-sampled diffusion-weighted images (DWIs) by directly predicting microstructural indices from sparsely-sampled DWIs. However, they implicitly make unrealistic
Externí odkaz:
http://arxiv.org/abs/2106.13188