Kyle Lafata
Overview
Explore the profile of Kyle Lafata including associated specialties, affiliations and a list of published articles.
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Articles
18
Citations
165
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0
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Recent Articles
1.
Uncertainty quantification in multi-parametric MRI-based meningioma radiotherapy target segmentation
Wang L, Yang Z, LaBella D, Reitman Z, Ginn J, Zhao J, et al.
Front Oncol
. 2025 Feb;
15:1474590.
PMID: 39935829
Purpose: This work investigates the use of a spherical projection-based U-Net (SPU-Net) segmentation model to improve meningioma segmentation performance and allow for uncertainty quantification. Methods: A total of 76 supratentorial...
2.
Kang J, Lafata K, Kim E, Yao C, Lin F, Rattay T, et al.
BMJ Oncol
. 2025 Jan;
3(1):e000134.
PMID: 39886165
Oncology is becoming increasingly personalised through advancements in precision in diagnostics and therapeutics, with more and more data available on both ends to create individualised plans. The depth and breadth...
3.
Chen Y, Wang B, Demeke D, Fan F, Berthier C, Mariani L, et al.
Clin J Am Soc Nephrol
. 2024 Dec;
PMID: 39714939
Background: Interstitial fibrosis and tubular atrophy (IFTA), and density and shape of peritubular capillaries (PTCs), are independently prognostic of disease progression. This study aimed to identify novel digital biomarkers of...
4.
Fan F, Liu Q, Zee J, Ozeki T, Demeke D, Yang Y, et al.
medRxiv
. 2024 Jul;
PMID: 39072032
Background: Visual scoring of tubular damage has limitations in capturing the full spectrum of structural changes and prognostic potential. We investigate if computationally quantified tubular features can enhance prognostication and...
5.
Yang Z, Lafata K, Vaios E, Hu Z, Mullikin T, Yin F, et al.
Med Phys
. 2023 Sep;
51(3):1931-1943.
PMID: 37696029
Background: Uncertainty quantification in deep learning is an important research topic. For medical image segmentation, the uncertainty measurements are usually reported as the likelihood that each pixel belongs to the...
6.
Konkel B, Macdonald J, Lafata K, Zaki I, Bozdogan E, Chaudhry M, et al.
Radiol Artif Intell
. 2023 Jun;
5(3):e220080.
PMID: 37293348
Purpose: To investigate the effect of training data type on generalizability of deep learning liver segmentation models. Materials And Methods: This Health Insurance Portability and Accountability Act-compliant retrospective study included...
7.
Yang Z, Hu Z, Ji H, Lafata K, Vaios E, Floyd S, et al.
Med Phys
. 2023 Feb;
50(8):4825-4838.
PMID: 36840621
Purpose: To develop a neural ordinary differential equation (ODE) model for visualizing deep neural network behavior during multi-parametric MRI-based glioma segmentation as a method to enhance deep learning explainability. Methods:...
8.
Kelleher C, Macdonald J, Jaffe T, Allen B, Kalisz K, Kauffman T, et al.
J Magn Reson Imaging
. 2023 Jan;
58(2):620-629.
PMID: 36607254
Background: The T w sequence is a standard component of a prostate MRI examination; however, it is time-consuming, requiring multiple signal averages to achieve acceptable image quality. Purpose/hypothesis: To determine...
9.
Ji H, Lafata K, Mowery Y, Brizel D, Bertozzi A, Yin F, et al.
Front Oncol
. 2022 Jun;
12:895544.
PMID: 35646643
Purpose: To develop a method of biologically guided deep learning for post-radiation FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information. Methods: Based on the classic reaction-diffusion...
10.
Chang Y, Jiang Z, Segars W, Zhang Z, Lafata K, Cai J, et al.
Phys Med Biol
. 2021 Jun;
66(11).
PMID: 34061044
. Synthesize realistic and controllable respiratory motions in the extended cardiac-torso (XCAT) phantoms by developing a generative adversarial network (GAN)-based deep learning technique.. A motion generation model was developed using...