Zobrazeno 1 - 10
of 142
pro vyhledávání: '"Parekh Vishwa"'
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
While Deep Reinforcement Learning has been widely researched in medical imaging, the training and deployment of these models usually require powerful GPUs. Since imaging environments evolve rapidly and can be generated by edge devices, the algorithm
Externí odkaz:
http://arxiv.org/abs/2306.05310
Deep reinforcement learning(DRL) is increasingly being explored in medical imaging. However, the environments for medical imaging tasks are constantly evolving in terms of imaging orientations, imaging sequences, and pathologies. To that end, we deve
Externí odkaz:
http://arxiv.org/abs/2306.00188
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
Federated learning is a recent development in the machine learning area that allows a system of devices to train on one or more tasks without sharing their data to a single location or device. However, this framework still requires a centralized glob
Externí odkaz:
http://arxiv.org/abs/2303.06783