Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Kulkarni, Pranav P."'
Quantum machine learning (QML) has the potential for improving the multi-label classification of rare, albeit critical, diseases in large-scale chest x-ray (CXR) datasets due to theoretical quantum advantages over classical machine learning (CML) in
Externí odkaz:
http://arxiv.org/abs/2405.00156
Autor:
Kulkarni, Pranav, Kanhere, Adway, Kukreja, Harshita, Zhang, Vivian, Yi, Paul H., Parekh, Vishwa S.
Generative Adversarial Network (GAN)-based synthesis of fat suppressed (FS) MRIs from non-FS proton density sequences has the potential to accelerate acquisition of knee MRIs. However, GANs trained on single-site data have poor generalizability to ex
Externí odkaz:
http://arxiv.org/abs/2404.07374
Autor:
Kulkarni, Pranav, Kanhere, Adway, Savani, Dharmam, Chan, Andrew, Chatterjee, Devina, Yi, Paul H., Parekh, Vishwa S.
Curating annotations for medical image segmentation is a labor-intensive and time-consuming task that requires domain expertise, resulting in "narrowly" focused deep learning (DL) models with limited translational utility. Recently, foundation models
Externí odkaz:
http://arxiv.org/abs/2403.15218
Autor:
Kulkarni, Pranav, Chan, Andrew, Navarathna, Nithya, Chan, Skylar, Yi, Paul H., Parekh, Vishwa S.
The proliferation of artificial intelligence (AI) in radiology has shed light on the risk of deep learning (DL) models exacerbating clinical biases towards vulnerable patient populations. While prior literature has focused on quantifying biases exhib
Externí odkaz:
http://arxiv.org/abs/2402.05713
Large-scale medical imaging datasets have accelerated development of artificial intelligence tools for clinical decision support. However, the large size of these datasets is a bottleneck for users with limited storage and bandwidth. Many users may n
Externí odkaz:
http://arxiv.org/abs/2307.00438
As the adoption of Artificial Intelligence (AI) systems within the clinical environment grows, limitations in bandwidth and compute can create communication bottlenecks when streaming imaging data, leading to delays in patient care and increased cost
Externí odkaz:
http://arxiv.org/abs/2305.15617
The Imaging Data Commons (IDC) is a cloud-based database that provides researchers with open access to cancer imaging data, with the goal of facilitating collaboration in medical imaging research. However, querying the IDC database for cohort discove
Externí odkaz:
http://arxiv.org/abs/2305.07637
Numerous large-scale chest x-ray datasets have spearheaded expert-level detection of abnormalities using deep learning. However, these datasets focus on detecting a subset of disease labels that could be present, thus making them distributed and non-
Externí odkaz:
http://arxiv.org/abs/2303.06180
With news and information being as easy to access as they currently are, it is more important than ever to ensure that people are not mislead by what they read. Recently, the rise of neural fake news (AI-generated fake news) and its demonstrated effe
Externí odkaz:
http://arxiv.org/abs/2302.00509
Segmentation is one of the most primary tasks in deep learning for medical imaging, owing to its multiple downstream clinical applications. However, generating manual annotations for medical images is time-consuming, requires high skill, and is an ex
Externí odkaz:
http://arxiv.org/abs/2301.07074