The need for medical artificial intelligence that incorporates prior images
The use of artificial intelligence (AI) has grown dramatically in the past few years in the
United States and worldwide, with more than 300 AI-enabled devices approved by the US …
United States and worldwide, with more than 300 AI-enabled devices approved by the US …
Learning spatio-temporal model of disease progression with NeuralODEs from longitudinal volumetric data
D Lachinov, A Chakravarty, C Grechenig… - … on Medical Imaging, 2023 - ieeexplore.ieee.org
Robust forecasting of the future anatomical changes inflicted by an ongoing disease is an
extremely challenging task that is out of grasp even for experienced healthcare …
extremely challenging task that is out of grasp even for experienced healthcare …
Prediction of lung nodule progression with an uncertainty-aware hierarchical probabilistic network
Predicting whether a lung nodule will grow, remain stable or regress over time, especially
early in its follow-up, would help doctors prescribe personalized treatments and better …
early in its follow-up, would help doctors prescribe personalized treatments and better …
Individualizing glioma radiotherapy planning by optimization of a data and physics informed discrete loss
The growth and progression of brain tumors is governed by patient-specific dynamics. Even
when the tumor appears well-delineated in medical imaging scans, tumor cells typically …
when the tumor appears well-delineated in medical imaging scans, tumor cells typically …
Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction
Q Liu, E Fuster-Garcia, IT Hovden… - arXiv preprint arXiv …, 2023 - arxiv.org
Diffuse gliomas are malignant brain tumors that grow widespread through the brain. The
complex interactions between neoplastic cells and normal tissue, as well as the treatment …
complex interactions between neoplastic cells and normal tissue, as well as the treatment …
[HTML][HTML] Radiomics and deep learning models for glioblastoma treatment outcome prediction based on tumor invasion modeling
Purpose We investigate the feasibility of using a biophysically guided approach for
delineating the Clinical Target Volume (CTV) in Glioblastoma Multiforme (GBM) by …
delineating the Clinical Target Volume (CTV) in Glioblastoma Multiforme (GBM) by …
Vestibular schwannoma growth prediction from longitudinal MRI by time-conditioned neural fields
Y Chen, JM Wolterink, OM Neve, SR Romeijn… - … Conference on Medical …, 2024 - Springer
Vestibular schwannomas (VS) are benign tumors that are generally managed by active
surveillance with MRI examination. To further assist clinical decision-making and avoid …
surveillance with MRI examination. To further assist clinical decision-making and avoid …
A Learnable Prior Improves Inverse Tumor Growth Modeling
Biophysical modeling, particularly involving partial differential equations (PDEs), offers
significant potential for tailoring disease treatment protocols to individual patients. However …
significant potential for tailoring disease treatment protocols to individual patients. However …
Image prediction of disease progression by style-based manifold extrapolation
Disease-modifying management aims to prevent deterioration and progression of the
disease, not just relieve symptoms. Unfortunately, the development of necessary therapies is …
disease, not just relieve symptoms. Unfortunately, the development of necessary therapies is …
A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling
Solving the inverse problem is the key step in evaluating the capacity of a physical model to
describe real phenomena. In medical image computing, it aligns with the classical theme of …
describe real phenomena. In medical image computing, it aligns with the classical theme of …