Zobrazeno 1 - 10
of 736
pro vyhledávání: '"Setlur, P."'
Autor:
Dimopoulos, George, Parra, Sabrina De Los Angeles Reverol, Mondal, Parmita, Udin, Michael, Williams, Kyle, Naghdi, Parisa, Rahmatpour, Ahmad, Nagesh, Swetadri Vasan Setlur, Bhurwani, Mohammad Mahdi Shiraz, Davies, Jason, Ionita, Ciprian N.
Subarachnoid hemorrhage (SAH), typically due to intracranial aneurysms, demands precise imaging for effective treatment. Digital Subtraction Angiography (DSA), despite being the gold standard, broadly visualizes cerebral blood flow, potentially maski
Externí odkaz:
http://arxiv.org/abs/2411.16637
Autor:
Naghdi, Parisa, Bhurwani, Mohammad Mahdi Shiraz, Rahmatpour, Ahmad, Mondal, Parmita, Udin, Michael, Williams, Kyle A, Nagesh, Swetadri Vasan Setlur, Ionita, Ciprian N
This study evaluates a multimodal machine learning framework for predicting treatment outcomes in intracranial aneurysms (IAs). Combining angiographic parametric imaging (API), patient biomarkers, and disease morphology, the framework aims to enhance
Externí odkaz:
http://arxiv.org/abs/2411.14407
Autor:
Mondal, Parmita, Williams, Kyle A, Naghdi, Parisa, Rahmatpour, Ahmad, Bhurwani, Mohammad Mahdi Shiraz, Nagesh, Swetadri Vasan Setlur, Ionita, Ciprian N
In intracranial aneurysm (IA) treatment, digital subtraction angiography (DSA) monitors device-induced hemodynamic changes. Quantitative angiography (QA) provides more precise assessments but is limited by hand-injection variability. This study evalu
Externí odkaz:
http://arxiv.org/abs/2411.14475
Autor:
Rahmatpour, Ahmad, Shields, Allison, Mondal, Parmita, Naghdi, Parisa, Udin, Michael, Williams, Kyle A, Bhurwani, Mohammad Mahdi Shiraz, Nagesh, Swetadri Vasan Setlur, Ionita, Ciprian N
This study leverages convolutional neural networks to enhance the temporal resolution of 3D angiography in intracranial aneurysms focusing on the reconstruction of volumetric contrast data from sparse and limited projections. Three patient-specific I
Externí odkaz:
http://arxiv.org/abs/2411.09632
Autor:
Kang, Katie, Setlur, Amrith, Ghosh, Dibya, Steinhardt, Jacob, Tomlin, Claire, Levine, Sergey, Kumar, Aviral
Despite the remarkable capabilities of modern large language models (LLMs), the mechanisms behind their problem-solving abilities remain elusive. In this work, we aim to better understand how the learning dynamics of LLM finetuning shapes downstream
Externí odkaz:
http://arxiv.org/abs/2411.07681
Autor:
Mondal, Parmita, Shields, Allison, Bhurwani, Mohammad Mahdi Shiraz, Williams, Kyle A, Nagesh, Swetadri Vasan Setlur, Siddiqui, Adnan H, Ionita, Ciprian N
This study aims to mitigate these biases and enhance QA analysis by applying a path-length correction (PLC) correction, followed by singular value decomposition (SVD)-based deconvolution, to angiograms obtained through both in-silico and in-vitro met
Externí odkaz:
http://arxiv.org/abs/2411.08185
Autor:
Mondal, Parmita, Nagesh, Swetadri Vasan Setlur, Sommers-Thaler, Sam, Shields, Allison, Bhurwani, Mohammad Mahdi Shiraz, Williams, Kyle A, Baig, Ammad, Snyder, Kenneth, Siddiqui, Adnan H, Levy, Elad, Ionita, Ciprian N
Intraoperative 2D quantitative angiography (QA) for intracranial aneurysms (IAs) has accuracy challenges due to the variability of hand injections. Despite the success of singular value decomposition (SVD) algorithms in reducing biases in computed to
Externí odkaz:
http://arxiv.org/abs/2411.03655
We introduce Visual Premise Proving (VPP), a novel task tailored to refine the process of chart question answering by deconstructing it into a series of logical premises. Each of these premises represents an essential step in comprehending a chart's
Externí odkaz:
http://arxiv.org/abs/2410.22492
Autor:
Setlur, Amrith, Nagpal, Chirag, Fisch, Adam, Geng, Xinyang, Eisenstein, Jacob, Agarwal, Rishabh, Agarwal, Alekh, Berant, Jonathan, Kumar, Aviral
A promising approach for improving reasoning in large language models is to use process reward models (PRMs). PRMs provide feedback at each step of a multi-step reasoning trace, potentially improving credit assignment over outcome reward models (ORMs
Externí odkaz:
http://arxiv.org/abs/2410.08146
Reconstructing 3D faces with facial geometry from single images has allowed for major advances in animation, generative models, and virtual reality. However, this ability to represent faces with their 3D features is not as fully explored by the facia
Externí odkaz:
http://arxiv.org/abs/2408.16907