no code implementations • 5 Feb 2025 • Guangyao Zheng, Michael A. Jacobs, Vladimir Braverman, Vishwa S. Parekh
Recently, self-supervised foundation models have been extended to three-dimensional (3D) computed tomography (CT) data, generating compact, information-rich embeddings with 1408 features that achieve state-of-the-art performance on downstream tasks such as intracranial hemorrhage detection and lung cancer risk forecasting.
1 code implementation • 28 Nov 2024 • Guangyao Zheng, Michael A. Jacobs, Vishwa S. Parekh
Our results indicate that the embeddings effectively encoded age and sex information, with a linear regression model achieving a root mean square error (RMSE) of 3. 8 years for age prediction and a softmax regression model attaining an AUC of 0. 998 for sex classification.
no code implementations • 8 Jun 2023 • Guangyao Zheng, Shuhao Lai, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh
While Deep Reinforcement Learning has been widely researched in medical imaging, the training and deployment of these models usually require powerful GPUs.
no code implementations • 31 May 2023 • Guangyao Zheng, Shuhao Lai, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh
Deep reinforcement learning(DRL) is increasingly being explored in medical imaging.
no code implementations • 12 Mar 2023 • Guangyao Zheng, Michael A. Jacobs, Vladimir Braverman, Vishwa S. Parekh
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.
no code implementations • 22 Feb 2023 • Guangyao Zheng, Samson Zhou, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh
Selective experience replay aims to recount selected experiences from previous tasks to avoid catastrophic forgetting.
2 code implementations • MIDL 2019 • Vishwa S. Parekh, Alex E. Bocchieri, Vladimir Braverman, Michael A. Jacobs
As a result, to develop a radiological decision support system, it would need to be equipped with potentially hundreds of deep learning models with each model trained for a specific task or organ.
no code implementations • 1 Aug 2019 • Alex E. Bocchieri, Vishwa S. Parekh, Kathryn R. Wagner. Shivani Ahlawat, Vladimir Braverman, Doris G. Leung, Michael A. Jacobs
A current clinical challenge is identifying limb girdle muscular dystrophy 2I(LGMD2I)tissue changes in the thighs, in particular, separating fat, fat-infiltrated muscle, and muscle tissue.
no code implementations • 10 Jun 2019 • Vishwa S. Parekh, John Laterra, Chetan Bettegowda, Alex E. Bocchieri, Jay J. Pillai, Michael A. Jacobs
Therefore, we applied our multiparametric radiomic framework (mpRadiomic) on 24 patients with brain tumors (8 grade II and 16 grade IV).
no code implementations • 8 Nov 2018 • Michael A. Jacobs, Christopher Umbricht, Vishwa Parekh, Riham El Khouli, Leslie Cope, Katarzyna J. Macura, Susan Harvey, Antonio C. Wolff
Results-The OncotypeDX classification by IRIS model had sensitivity of 95% and specificity of 89% with AUC of 0. 92.
no code implementations • 25 Oct 2018 • Vishwa S. Parekh, Michael A. Jacobs
Radiomics is a rapidly growing field that deals with modeling the textural information present in the different tissues of interest for clinical decision support.
no code implementations • 25 Sep 2018 • Vishwa S. Parekh, Michael A. Jacobs
The use of radiomics for quantitative extraction of textural features from radiological imaging is increasing moving towards clinical decision support.
no code implementations • 10 Feb 2018 • Vishwa S. Parekh, Katarzyna J. Macura, Susan Harvey, Ihab Kamel, Riham EI-Khouli, David A. Bluemke, Michael A. Jacobs
For example, using a deep learning network, we developed and tested a multiparametric deep learning (MPDL) network for segmentation and classification using multiparametric magnetic resonance imaging (mpMRI) radiological images.
no code implementations • 13 Jun 2016 • Vishwa S. Parekh, Jeremy R. Jacobs, Michael A. Jacobs
On analyzing the performance of these methods, we observed that there was a high of similarity between multiparametric embedded images from NLDR methods and the ADC map and perfusion map.