no code implementations • 9 Mar 2019 • Alexandros Karargyris, Satyananda Kashyap, Joy T. Wu, Arjun Sharma, Mehdi Moradi, Tanveer Syeda-Mahmood
Age prediction based on appearances of different anatomies in medical images has been clinically explored for many decades.
no code implementations • 10 Mar 2019 • Satyananda Kashyap, Honghai Zhang, Karan Rao, Milan Sonka
4D LOGISMOS validation on 108 MRIs from baseline and 12 month follow-up scans of 54 patients showed a significant reduction in segmentation errors (\emph{p}$<$0. 01) compared to 3D.
no code implementations • 10 Mar 2019 • Satyananda Kashyap, Honghai Zhang, Milan Sonka
State-of-the-art automated segmentation algorithms are not 100\% accurate especially when segmenting difficult to interpret datasets like those with severe osteoarthritis (OA).
no code implementations • 10 Mar 2019 • Satyananda Kashyap, Ipek Oguz, Honghai Zhang, Milan Sonka
We present a fully automated learning-based approach for segmenting knee cartilage in the presence of osteoarthritis (OA).
no code implementations • 21 Jun 2019 • Tanveer Syeda-Mahmood, Hassan M. Ahmad, Nadeem Ansari, Yaniv Gur, Satyananda Kashyap, Alexandros Karargyris, Mehdi Moradi, Anup Pillai, Karthik Sheshadri, Wei-Ting Wang, Ken C. L. Wong, Joy T. Wu
Chest X-rays are the most common diagnostic exams in emergency rooms and hospitals.
no code implementations • 26 Jul 2019 • Jayaraman J. Thiagarajan, Satyananda Kashyap, Alexandros Karagyris
Weakly supervised instance labeling using only image-level labels, in lieu of expensive fine-grained pixel annotations, is crucial in several applications including medical image analysis.
no code implementations • 3 May 2020 • Deepta Rajan, Jayaraman J. Thiagarajan, Alexandros Karargyris, Satyananda Kashyap
Automated diagnostic assistants in healthcare necessitate accurate AI models that can be trained with limited labeled data, can cope with severe class imbalances and can support simultaneous prediction of multiple disease conditions.
no code implementations • 27 Jul 2020 • Tanveer Syeda-Mahmood, Ken C. L. Wong, Yaniv Gur, Joy T. Wu, Ashutosh Jadhav, Satyananda Kashyap, Alexandros Karargyris, Anup Pillai, Arjun Sharma, Ali Bin Syed, Orest Boyko, Mehdi Moradi
Obtaining automated preliminary read reports for common exams such as chest X-rays will expedite clinical workflows and improve operational efficiencies in hospitals.
no code implementations • 2 Aug 2020 • Satyananda Kashyap, Alexandros Karargyris, Joy Wu, Yaniv Gur, Arjun Sharma, Ken C. L. Wong, Mehdi Moradi, Tanveer Syeda-Mahmood
Deep learning has now become the de facto approach to the recognition of anomalies in medical imaging.
no code implementations • 4 Aug 2020 • Sandesh Ghimire, Satyananda Kashyap, Joy T. Wu, Alexandros Karargyris, Mehdi Moradi
Through pneumonia-classification experiments on multi-source chest X-ray datasets, we show that this algorithm helps in improving classification accuracy on a new source of X-ray dataset.
1 code implementation • 15 Sep 2020 • Alexandros Karargyris, Satyananda Kashyap, Ismini Lourentzou, Joy Wu, Arjun Sharma, Matthew Tong, Shafiq Abedin, David Beymer, Vandana Mukherjee, Elizabeth A. Krupinski, Mehdi Moradi
We report deep learning experiments that utilize the attention maps produced by eye gaze dataset to show the potential utility of this data.
no code implementations • 22 Mar 2021 • Ken C. L. Wong, Satyananda Kashyap, Mehdi Moradi
By imposing L1 regularization and thresholding on the scaling weights, this framework iteratively removes unnecessary feature channels from a pre-trained model.
1 code implementation • 31 Jul 2021 • Joy T. Wu, Nkechinyere N. Agu, Ismini Lourentzou, Arjun Sharma, Joseph A. Paguio, Jasper S. Yao, Edward C. Dee, William Mitchell, Satyananda Kashyap, Andrea Giovannini, Leo A. Celi, Mehdi Moradi
Despite the progress in automatic detection of radiologic findings from chest X-ray (CXR) images in recent years, a quantitative evaluation of the explainability of these models is hampered by the lack of locally labeled datasets for different findings.
no code implementations • 6 Aug 2021 • Ken C. L. Wong, Satyananda Kashyap, Mehdi Moradi
Network-based transfer learning allows the reuse of deep learning features with limited data, but the resulting models can be unnecessarily large.
1 code implementation • 29 Sep 2021 • Alexandros Karargyris, Renato Umeton, Micah J. Sheller, Alejandro Aristizabal, Johnu George, Srini Bala, Daniel J. Beutel, Victor Bittorf, Akshay Chaudhari, Alexander Chowdhury, Cody Coleman, Bala Desinghu, Gregory Diamos, Debo Dutta, Diane Feddema, Grigori Fursin, Junyi Guo, Xinyuan Huang, David Kanter, Satyananda Kashyap, Nicholas Lane, Indranil Mallick, Pietro Mascagni, Virendra Mehta, Vivek Natarajan, Nikola Nikolov, Nicolas Padoy, Gennady Pekhimenko, Vijay Janapa Reddi, G Anthony Reina, Pablo Ribalta, Jacob Rosenthal, Abhishek Singh, Jayaraman J. Thiagarajan, Anna Wuest, Maria Xenochristou, Daguang Xu, Poonam Yadav, Michael Rosenthal, Massimo Loda, Jason M. Johnson, Peter Mattson
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience.
no code implementations • 18 Nov 2022 • Ashutosh Jadhav, Satyananda Kashyap, Hakan Bulu, Ronak Dholakia, Amon Y. Liu, Tanveer Syeda-Mahmood, William R. Patterson, Hussain Rangwala, Mehdi Moradi
Residual flow into the sac after the intervention is a failure that could be due to the use of an undersized device, or to vascular anatomy and clinical condition of the patient.