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.
1 code implementation • 8 Aug 2022 • Gaurang Karwande, Amarachi Mbakawe, Joy T. Wu, Leo A. Celi, Mehdi Moradi, Ismini Lourentzou
Despite the progress in utilizing deep learning to automate chest radiograph interpretation and disease diagnosis tasks, change between sequential Chest X-rays (CXRs) has received limited attention.
1 code implementation • 10 Jan 2022 • Ken C. L. Wong, Mehdi Moradi
For existing loss functions for multi-class image segmentation, there is usually a tradeoff among accuracy, robustness to hyperparameters, and manual weight selections for combining different losses.
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 • 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 • 20 May 2021 • Nkechinyere N. Agu, Joy T. Wu, Hanqing Chao, Ismini Lourentzou, Arjun Sharma, Mehdi Moradi, Pingkun Yan, James Hendler
Radiologists usually observe anatomical regions of chest X-ray images as well as the overall image before making a decision.
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.
no code implementations • 1 Mar 2021 • Ottmar Cronie, Mehdi Moradi, Christophe A. N. Biscio
This paper presents the first general (supervised) statistical learning framework for point processes in general spaces.
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 • 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.
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 • 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 • 12 Sep 2019 • Ken C. L. Wong, Mehdi Moradi
Deep learning has largely reduced the need for manual feature selection in image segmentation.
no code implementations • 21 Jun 2019 • Alexandros Karargyris, Ken C. L. Wong, Joy T. Wu, Mehdi Moradi, Tanveer Syeda-Mahmood
We experiment with two different deep neural network architectures and show that the proposed method results in a large improvement in the performance of the classifiers, specially on negated findings.
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 • 2 Apr 2019 • Ken C. L. Wong, Mehdi Moradi, Joy Wu, Tanveer Syeda-Mahmood
In this work, we report a deep neural network trained for classifying CXRs with the goal of identifying a large number of normal (disease-free) images without risking the discharge of sick patients.
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 • 5 Sep 2018 • Mehdi Moradi, Ali Madani, Yaniv Gur, Yufan Guo, Tanveer Syeda-Mahmood
The source of big data is typically large image collections and clinical reports recorded for these images.
1 code implementation • 31 Aug 2018 • Ken C. L. Wong, Mehdi Moradi, Hui Tang, Tanveer Syeda-Mahmood
In this paper, we propose a network architecture and the corresponding loss function which improve segmentation of very small structures.
no code implementations • 15 Aug 2018 • Ken C. L. Wong, Tanveer Syeda-Mahmood, Mehdi Moradi
Deep learning has shown promising results in medical image analysis, however, the lack of very large annotated datasets confines its full potential.
no code implementations • 7 May 2018 • Ken C. L. Wong, Alexandros Karargyris, Tanveer Syeda-Mahmood, Mehdi Moradi
We train a discriminative segmentation model only on normal images to provide a source of knowledge to be transferred to a disease detection classifier.